Original Research Paper Insurance pricing

Life Insurance Premium Pricing Through Personalized Mortality Table Using the Wang Premium Principle

Amirmohammad Norouzi; Mojtaba Ranjbar; Saman Vahabi; Mitra Ghanbarzadeh

Volume 15, Issue 3 , June 2026, Pages 205-226

https://doi.org/10.22056/ijir.2026.03.01

Abstract

BACKGROUND AND OBJECTIVES: The conventional approach to life insurance premium pricing relies on standardized mortality tables, which apply uniform survival probabilities to diverse policyholder populations. While this method is computationally straightforward, it overlooks the heterogeneity of individual ... Read More BACKGROUND AND OBJECTIVES: The conventional approach to life insurance premium pricing relies on standardized mortality tables, which apply uniform survival probabilities to diverse policyholder populations. While this method is computationally straightforward, it overlooks the heterogeneity of individual risk profiles, often resulting in premiums that fail to reflect the true mortality risk of each insured individual. This may lead to pricing inefficiencies, a sense of unfairness among low-risk policyholders and unintended cross-subsidization between high- and low-risk groups where low-risk individuals may overpay to offset losses from higher-risk counterparts. To address these challenges, this study develops a novel framework for constructing personalized mortality tables tailor-made to the unique risk characteristics of each policyholder. By integrating these individualized tables with the Wang premium principle, enhanced by advanced machine learning techniques, the research aims to deliver fairer, more precise, and actuarially sound premium calculations. This approach not only responds to the evolving expectations of modern insurance markets but also supports long-term profitability by retaining low-risk clients through transparent and equitable pricing practices.METHODS: This study adopts a developmental–applied approach with an analytical design to create risk-adjusted mortality tables reflecting individual policyholder characteristics. Central to the methodology is the development of a composite market risk parameter (λ), which consolidates key risk factors—including age, gender, smoking status, occupation, medical history, contract duration, and death benefit—into a single predictive index. This parameter serves as the cornerstone for the Wang distortion function, a robust mathematical tool that transforms classical survival probabilities into risk-adjusted probabilities, incorporating risk aversion and market dynamics. The estimation of λ leverages historical policyholder data, capturing a comprehensive set of demographic, health, and occupational variables. Two state-of-the-art machine learning algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—are deployed to predict λ with high accuracy, capitalizing on their ability to model complex, non-linear relationships among risk factors and handle noisy or heterogeneous data effectively. All data preprocessing, model training, and hyperparameter optimization are conducted using Python, ensuring computational efficiency, scalability, and reproducibility. The predicted λ values are then integrated into the Wang premium principle to compute individualized premium rates, ensuring that pricing reflects the unique risk profile of each policyholder and aligns with actuarial expectations.FINDINGS: Empirical results underscore the superiority of the proposed framework in delivering equitable and precise premium estimates. By aggregating risk factors into the market risk parameter, the model generates personalized survival probabilities that replace the uniform probabilities of classical mortality tables, offering a more granular and accurate representation of mortality risk. For high-risk policyholders with elevated λ values, premiums calculated using the Wang principle are significantly higher than those derived from traditional methods, ensuring that pricing accurately reflects increased mortality risks. Conversely, low-risk individuals benefit from lower premiums, mitigating the issue of overpricing that often affects this group in conventional frameworks. Feature importance analysis, conducted through the machine learning models, reveals that health-related factors such as medical history and smoking status alongside age, exert the strongest influence on predicting λ, driving significant variability in premium rates. Occupation and gender, while less dominant, contribute meaningfully to risk assessment, highlighting the multidimensional nature of mortality risk modeling. The Random Forest model achieves a mean squared error (MSE) of 0.00028, indicating high predictive accuracy and robustness. These findings demonstrate the framework’s ability to eliminate hidden subsidies between risk groups, ensuring actuarially fair pricing that aligns with individual risk profiles and market realities.CONCLUSION: The principal contribution of this research lies in its development of a hybrid framework that seamlessly integrates personalized mortality tables with the Wang premium principle, enhanced by machine learning-driven predictions. By aligning premiums with individual risk profiles, the model ensures actuarial fairness, reduces inefficiencies, and enhances pricing transparency, fostering greater policyholder trust and reducing lapse rates in competitive markets. The findings confirm a consistent pattern: Wang-based premiums exceed classical premiums for high-risk individuals and are lower for low-risk groups, ensuring equitable differentiation across risk profiles. From a practical perspective, insurers adopting this framework can strengthen customer relationships, improve retention of low-risk policyholders, and enhance market competitiveness. Future research could extend this framework to other insurance branches, such as health or property insurance, where personalized risk assessment could yield similar benefits. Incorporating additional data sources, such as behavioral or lifestyle data from wearable devices or financial records, could further refine risk predictions. Exploring hybrid models that combine deep learning with traditional actuarial methods holds promise for enhancing predictive performance, particularly for complex or sparse datasets. A critical next step is validating the model with real-world insurance data, which would provide a robust basis for assessing its practical effectiveness and scalability in diverse market conditions. By modernizing life insurance pricing, this research paves the way for a more equitable, transparent, and customer-centric insurance industry, responding to the growing demand for fairness and precision in pricing practices.

Original Research Paper Risk management in the insurance industry

Advanced Analysis of Transportation Data Using Machine Learning: Bill of Lading Insurance Premium Prediction

Zeinab Jafarzadeh; Narges Mirehi; Effat Golpar Raboky

Volume 15, Issue 3 , June 2026, Pages 227-238

https://doi.org/10.22056/ijir.2026.03.02

Abstract

BACKGROUND AND OBJECTIVES: Intelligent transportation and logistics management are critical components of modern supply chains, directly influencing cost reduction and risk management. With the rapid increase in transportation data, the need for data-driven approaches to financial decision-making has ... Read More BACKGROUND AND OBJECTIVES: Intelligent transportation and logistics management are critical components of modern supply chains, directly influencing cost reduction and risk management. With the rapid increase in transportation data, the need for data-driven approaches to financial decision-making has become increasingly evident. One of the main challenges in this field is the non-linear nature of relationships in premium calculations, which traditional models fail to adequately capture. This research aims to transition from conventional methods to an intelligent insurance pricing system in Qom Province, Iran—recognized as a major transit hub and the logistics center of the country. The significance of this study lies in its ability to uncover non-linear patterns within large-scale datasets that traditional models are unable to analyze effectively. The operational benefits of this system go beyond mere numerical predictions; unlike them, it includes real-time financial risk management, increased tariff transparency, and the intelligent detection of anomalies in the bill of lading issuance process. These features contribute to optimized budgeting and improved decision-making accuracy in this critical transportation bottleneck.METHODS: This data-driven research focuses on over 600,000 bills of lading records from Qom Province. In the first stage, the data underwent thorough cleaning and outlier management, followed by the identification of key variables that influence insurance premiums such as cargo weight, transportation distance, and product type. Due to the high complexity and non-linear relationships among these variables, traditional linear models were unable to capture the underlying pricing patterns. To address this limitation, advanced algorithms including Symmetric Gradient Boosting (Categorical Boosting) and Extreme Gradient Boosting (XGBoost) were deployed. The Symmetric Gradient Boosting model was chosen as the core algorithm due to its superior ability to handle categorical variables (such as cargo type) and its robust performance in preventing overfitting. In the final stage, the models’ performance was assessed using statistical metrics, including the Coefficient of Determination (R2) and Median Absolute Error (MedAE), to evaluate the precision of the predicted values.FINDINGS: The analysis revealed that intelligent algorithms significantly improve the accuracy of insurance premium calculations. The primary innovation of this research is the identification of hidden non-linear and multi-dimensional patterns within large-scale logistics data. The Symmetric Gradient Boosting model achieved an R2 score of 0.86, accurately reconstructing market fluctuations with high precision. This result suggests that the model can replace traditional methods, which, due to their rigid structure, lack the flexibility to adapt to the complexities of the logistics industry. By leveraging advanced machine learning techniques, this study demonstrated that insurance pricing is governed by non-linear relationships rather than simple linear dependencies. Furthermore, the research showed that qualitative factors such as cargo type and the identity of the transportation companies play a far more significant role in financial risk management than before. Traditional models typically placed heavy emphasis on quantitative variables such as distance, but this study demonstrated that categorical variables could influence pricing much more significantly.CONCLUSION: A key strategic takeaway from this study is the identification of cargo nature and the identity of transportation companies as the primary factors influencing insurance risk assessment. Unlike traditional methods, which predominantly focus on distance, the findings from this research suggest that qualitative parameters such as the type of goods being transported and the reliability of the transport company play a far more substantial role in managing financial risk. The intelligent architecture developed in this study minimizes prediction errors, making it possible to implement dynamic and fair pricing mechanisms. This not only ensures the protection of the interests of insurance companies but also safeguards the rights of cargo owners, thereby creating a more balanced and equitable system for all stakeholders. By moving beyond outdated pricing models, this intelligent system provides a foundation for a more transparent, efficient, and responsive insurance pricing model in the transportation and logistics sector. Moreover, the proposed system offers real-time capabilities for financial risk management and anomaly detection, enhancing operational efficiency and reducing human error. This is particularly crucial in sectors where financial decision-making is complex and data-heavy, such as logistics and transportation. The system also facilitates the detection of potential discrepancies in bill of lading issuance, thereby reducing fraudulent activities and ensuring compliance with industry standards. These capabilities can have a far-reaching impact on the logistics sector, improving the transparency and accountability of transactions while also supporting better budgeting and forecasting.

Original Research Paper New Insurance Technologies

An Analysis of the Antecedents and Consequences of Knowledge Absorptive Capacity in the Insurance Industry

Shabnam Refoua; Hosseinali Bakhtiar Nasrabadi

Volume 15, Issue 3 , June 2026, Pages 239-252

https://doi.org/10.22056/ijir.2026.03.03

Abstract

BACKGROUND AND OBJECTIVES: Due to the data‑driven and knowledge‑based nature of insurance industry, the development of this industry depends on enhancing its absorptive capacity, i.e., the ability to acquire, analyze, combine, and apply knowledge. Despite the importance of knowledge absorption for ... Read More BACKGROUND AND OBJECTIVES: Due to the data‑driven and knowledge‑based nature of insurance industry, the development of this industry depends on enhancing its absorptive capacity, i.e., the ability to acquire, analyze, combine, and apply knowledge. Despite the importance of knowledge absorption for improving competitiveness and service quality in insurance, systematic and integrated efforts to strengthen absorptive capacity have been neglected. Therefore, the objective of this research is to identify and explain the antecedents and consequences of absorptive capacity in the Iranian insurance industry.METHODS: The present study adopted qualitative research design and employed the thematic analysis approach proposed by Braun and Clarke (2006) as its primary analytical strategy. This method was selected due to its flexibility and suitability for systematically capturing latent patterns of meaning within complex and context‑dependent phenomena such as knowledge absorptive capacity in a highly regulated service industry. Data were collected through an in‑depth analysis of specialized documents and authoritative texts, including policy documents, regulatory guidelines, industry reports, academic publications, and written accounts reflecting managerial experiences, organizational structures, and knowledge‑related transformations within the Iranian insurance industry. The analytical process followed the six recursive phases of thematic analysis: familiarization with the data, generation of initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the final analytical narrative. To strengthen analytical rigor, coding was conducted iteratively and comparatively, allowing for continuous refinement of themes across data sources. Data interpretation was guided by a paradigmatic analytical framework incorporating causal conditions, contextual factors, intervening conditions, and consequences. Within this framework, basic themes were first identified and subsequently aggregated into organizing themes and higher‑order global themes, ensuring conceptual coherence and theoretical depth. To enhance analytical richness and reinforce interpretive credibility, the emergent findings were systematically compared with both domestic and international studies on absorptive capacity, organizational learning, and knowledge management. This comparative analysis enabled the identification of convergences and divergences between the Iranian insurance context and dominant theoretical models, thereby strengthening the explanatory power and contextual sensitivity of the findings. All in all, this methodological approach ensured transparency in the analytical process and contributed to the robustness, transferability, and theoretical contribution of the study.FINDINGS: The findings of this study demonstrate that absorptive capacity in the Iranian insurance industry, while conceptually consistent with classical and contemporary frameworks in the international literature, differs markedly in its formation, trajectory of development, and pattern of outcomes. Rather than emerging as a proactive and deliberately cultivated strategic capability, absorptive capacity in this context is predominantly reactive, activated by environmental turbulence, intensified competition, technological disruption, and persistent knowledge gaps. Consequently, knowledge-related initiatives tend to be fragmented, short-term, and heavily dependent on managerial attitudes and stability, preventing absorptive capacity from maturing into an institutionalized and sustainable organizational capability. From a contextual and institutional perspective, the study reveals that managerial perceptions of knowledge, short managerial tenures, weak merit-based systems, and the absence of organizational memory significantly constrain the development of absorptive capacity. These challenges are reinforced by regulatory passivity and the lack of explicit knowledge-oriented requirements within the supervisory framework governing the insurance industry. This configuration contrasts sharply with the dominant findings in global literature, which emphasizes the facilitative role of supportive institutional environments in enhancing organizational learning and innovation. In the Iranian insurance context, therefore, institutional arrangements function less as enablers and more as structural inhibitors of systematic knowledge absorption. At the operational and process level, deficiencies in data infrastructure, limited access to reliable information, weak knowledge-sharing mechanisms, and cultural resistance to change further hinder the progression of absorptive capacity across its acquisition, assimilation, transformation, and exploitation phases. As a result, knowledge absorption frequently remains confined to individual awareness or isolated initiatives, failing to translate into coordinated organizational action and long-term capability development. Unlike evidence from technology-intensive industries, digitalization in this setting has not yet assumed a catalytic role in strengthening absorptive capacity, but instead highlights a critical missing link between data availability and knowledge utilization. With respect to outcomes, the findings indicate that the effects of absorptive capacity in the Iranian insurance industry materialize primarily through incremental and process-based improvements rather than radical technological innovations. These outcomes include enhanced underwriting and claims processes, improved managerial decision-making quality, greater cognitive and strategic agility, strengthened process-driven competitive advantage, and improved customer experience. This pattern suggests that, within highly regulated service industries such as insurance, absorptive capacity serves less as a driver of rapid technological breakthroughs and more as a foundational mechanism for continuous learning, organizational resilience, and gradual value creation. Accordingly, the direct transfer of dominant global theoretical models to such contexts without sufficient attention to institutional and cultural contingencies may yield incomplete or misleading conclusions.CONCLUSION: The findings of the study indicate that although absorptive capacity in the Iranian insurance industry is conceptually aligned with the international literature, its mode of realization and the nature of its outcomes are gradual, processual, and highly dependent on the institutional and regulatory context. These results highlight that the straightforward generalization of dominant absorptive capacity models to regulated service industries may be misleading. In insurance industry, the development of absorptive capacity relies less on rapid technological innovation and more on strengthening organizational learning, managerial coherence, and a systematic knowledge-oriented perspective.

Original Research Paper Financial wealth

Forecasting Changes in Financial Solvency of Insurance Companies Using Financial Soundness Indicators

Mohsen Gharahkhani; Marjan Gharahkhani; Feryal Farakesh

Volume 15, Issue 3 , June 2026, Pages 253-266

https://doi.org/10.22056/ijir.2026.03.04

Abstract

BACKGROUND AND OBJECTIVES: The financial solvency and sustainability of insurance companies is widely recognized as a fundamental cornerstone of financial system stability, given the central role insurers play in risk transfer, loss absorption, and long-term capital allocation. Solvent insurance institutions ... Read More BACKGROUND AND OBJECTIVES: The financial solvency and sustainability of insurance companies is widely recognized as a fundamental cornerstone of financial system stability, given the central role insurers play in risk transfer, loss absorption, and long-term capital allocation. Solvent insurance institutions contribute to economic resilience by protecting policyholders, limiting contagion effects, and reinforcing trust in financial markets. International experience, particularly during episodes of financial disstress and insurance market disruptions, has shown that weaknesses in insurers’ financial soundness can propagate systemic risk, amplify macroeconomic shocks, and undermine the effectiveness of regulatory frameworks. As a result, supervisory authorities increasingly emphasize the development of early-warning mechanisms and analytical tools capable of monitoring and forecasting insurers’ financial conditions in a timely and reliable manner. In this context, identifying the determinants of insurers’ financial solvency and constructing empirically robust predictive models have become key research priorities. While several internationally recognized frameworks—such as Risk-Based Capital Systems, Solvency II, and Financial Soundness Indicators—have been developed and applied across advanced insurance markets, their empirical adaptation to emerging and developing insurance systems remains limited. In Iran, despite the expansion of the insurance industry, increased private sector participation, and greater product complexity, empirical studies that integrate global solvency frameworks with firm-level domestic data are still relatively scarce. Accordingly, the primary objective of this study is to identify the key financial factors influencing the solvency of Iranian insurance companies and to develop an analytical model for predicting their financial condition based on internationally accepted financial soundness frameworks.METHODS: This study adopts a quantitative approach grounded in established international solvency assessment methodologies. A comprehensive set of indicators was constructed using the CARAMELS framework and Financial Soundness Indicators (FSIs), capturing critical dimensions of insurers’ financial health, including capital adequacy, asset quality, reinsurance structure, actuarial soundness, management efficiency, profitability, liquidity, and sensitivity to risk. Financial data were extracted from the audited financial statements of Iranian insurance companies over the study period and subjected to rigorous preprocessing procedures, including data validation, standardization, normality assessment, and the mitigation of multicollinearity among explanatory variables. Given the binary nature of the dependent variable indicating improvement or deterioration in financial solvency, a logistic regression model was employed to estimate the probability of maintaining adequate solvency. Model parameters were estimated using maximum likelihood techniques, and model adequacy was evaluated through standard goodness-of-fit measures and classification performance indicators. To assess the robustness and stability of the estimated relationships, a sensitivity analysis was conducted by examining changes in coefficient estimates and predictive outcomes under alternative specifications. This methodological design allows both explanatory insight into solvency drivers and practical predictive capability for supervisory use.FINDINGS: The empirical results indicate that the estimated model demonstrates a strong predictive performance and satisfactory explanatory power. The final specification achieves an overall prediction accuracy of 84 percent, with a Pseudo R² value of 0.42, suggesting a substantial improvement over baseline models and confirming the relevance of the selected financial soundness indicators. Capital adequacy variables exhibit a positive and statistically significant relationship with financial solvency, highlighting the importance of strong equity positions and sufficient capital buffers in enhancing insurers’ resilience. Profitability measures, particularly return on assets (ROA), also show a significant positive effect on solvency, reflecting the role of efficient asset utilization and sustainable earnings generation in supporting long-term financial stability. Liquidity-related indicators similarly exert a positive influence, underscoring the importance of maintaining adequate liquid resources to meet short-term obligations and absorb unexpected shocks. In contrast, variables associated with insurance risk exposures such as outstanding claims reserves and policyholder-related liabilities demonstrate statistically significant negative effects on solvency. These results suggest that excessive reserve burdens and growing insurance liabilities can materially weaken insurers’ financial positions if not properly managed. The sensitivity analysis confirms the stability of the estimated coefficients, as both the direction and relative magnitude of key effects remain consistent across alternative model specifications.CONCLUSION: The findings of this study indicate that capital strength, profitability, and effective liquidity management are central determinants of financial solvency in the Iranian insurance industry, while insufficient control of insurance-related liabilities poses a significant risk to financial stability. By integrating internationally recognized financial soundness frameworks with localized firm-level data, this research provides a practical and analytically robust model for monitoring and forecasting insurers’ financial conditions. The proposed approach offers valuable implications for regulatory authorities seeking to strengthen early-warning systems, for insurance company managers engaged in strategic financial planning, and for policymakers aiming to enhance the resilience and stability of the national insurance system. Overall, the study demonstrates that internationally established solvency assessment frameworks can be effectively adapted to emerging insurance markets when combined with appropriate econometric techniques and context-specific data. The results provide a solid empirical foundation for future research and for the development of more advanced, data-driven solvency monitoring tools in the insurance industry.

Original Research Paper Insurance rights

Challenges of Iran's Insurance System in Covering Autonomous Vehicle Risks: Limitations and Reform Requirements

Hamid Afkar

Volume 15, Issue 3 , June 2026, Pages 267-280

https://doi.org/10.22056/ijir.2026.03.05

Abstract

Background and Objectives: Autonomous Vehicles (AVs) have emerged as one of the most transformative innovations in the global transportation sector, reshaping traditional concepts of driving, liability, and risk management. While these technologies promise improved safety and efficiency, they also ... Read More Background and Objectives: Autonomous Vehicles (AVs) have emerged as one of the most transformative innovations in the global transportation sector, reshaping traditional concepts of driving, liability, and risk management. While these technologies promise improved safety and efficiency, they also introduce complex legal and insurance challenges, particularly in jurisdictions such as Iran, where the third-party liability insurance framework is built on the notion of drivers’ faults. As AVs rely on artificial intelligence and automated systems rather than human operation, determining liability in the event of an accident becomes increasingly complicated. Technical malfunctions, software defects, and cyberattacks pose new categories of risk that the current insurance system is not adequately prepared to address. This study aims to examine the structural limitations of Iran’s insurance system in covering the risks associated with AVs and to propose a set of legal and regulatory reforms capable of aligning the national framework with international standards and emerging technologies. The objective is to develop a coherent policy model that both protects victims and ensures the long-term sustainability of the insurance market in the era of intelligent mobility.METHODS: This research adopts an analytical–descriptive methodology based on the critical examination of Iran’s current insurance laws, particularly the 2016 Act on Compulsory Third-Party Liability Insurance, and its practical inadequacy in dealing with automated driving systems. The study systematically analyzes the conceptual and legal gaps in attributing liability to non-human agents, such as vehicle manufacturers, software developers, and AI system designers. In addition, it explores comparative international experiences in regulating autonomous vehicle insurance frameworks, drawing insights from legal reforms implemented in Europe, the United Kingdom, Germany, Japan, and the United States. The analysis emphasizes both doctrinal and practical dimensions: (1) the evolution of liability doctrines from fault-based to strict or hybrid liability systems; (2) the emergence of specialized insurance products, including product liability and cyber insurance; and (3) the need for data-driven risk assessment and accident reconstruction mechanisms, such as the mandatory use of vehicle “black boxes.” Through this analytical lens, the research identifies critical deficiencies and formulates policy-oriented recommendations suitable for Iran’s legal and insurance context.FINDINGS: The study reveals that the current third-party insurance framework in Iran is structurally ill-equipped to accommodate the risks posed by AVs . The primary deficiencies include the difficulty of determining faults when accidents stem from algorithmic errors or mechanical failures, the absence of effective recovery mechanisms against manufacturers or software providers, and the lack of specialized insurance products tailormade to technological risks. Furthermore, existing regulatory mechanisms fail to provide adequate standards for assessing AV-related risks or for calculating insurance premiums. This inadequacy is exacerbated by the limited financial resilience of insurers and the absence of coherent cooperation among regulatory institutions, insurers, automakers, and technology firms. The study also finds that Iran’s current approach — which places liability primarily on drivers — is inconsistent with the technological reality of autonomous systems, where human control is minimized or eliminated. Comparative analysis demonstrates that other jurisdictions have addressed similar issues through a combination of strict manufacturer liability, dual-insurance systems distinguishing between human-operated and automated modes, establishment of compensation funds for untraceable risks, and the integration of advanced data monitoring systems for accident verification. In contrast, Iran’s lack of legislative foresight, technical infrastructure, and expert capacity in interpreting AV-related accidents creates legal ambiguity and procedural delays that undermine both victim compensation and market stability. Moreover, the study highlights the ethical and cyber dimensions of AV risk, emphasizing that without transparent data-sharing mechanisms and cybersecurity coverage, insurers cannot accurately evaluate or price risk exposure. These challenges collectively underscore the urgent need for an adaptive insurance ecosystem capable of addressing multidimensional risks.CONCLUSION: The findings underscore the necessity of a comprehensive reform of Iran’s third-party liability insurance laws to ensure effective coverage for AV risks. The study proposes a paradigm shift from the traditional fault-based model of civil liability toward a hybrid or strict liability framework that attributes responsibility to manufacturers and technology developers where appropriate. It recommends the development of specialized insurance products — including product liability, cyber, and data-driven policies — designed to distribute risks more equitably among stakeholders. Additionally, the creation of a national database for recording and analyzing autonomous vehicle accidents, the mandatory installation of event data recorders, and the introduction of transparent data-sharing obligations are essential for accurate accident assessment and liability allocation. Institutional coordination among legislators, the Central Insurance of Iran, technology companies, and the automotive industry is vital for implementing these reforms. By adopting a forward-looking regulatory model, Iran can foster innovation while ensuring victim protection and financial sustainability in its insurance market. Ultimately, reforming the insurance framework in alignment with technological progress is not merely a legal necessity but a strategic imperative for integrating autonomous vehicles into Iran’s transportation system in a safe, ethical, and economically viable manner.

Original Research Paper Industrial organization of insurance markets

Adapting Coopetition Strategy Models from Other Industries to the Platform Ecosystem of the Insurance Industry: A Systematic Review

Mehdi Haghighikafash; Vahid Khashei; Shahram Khalil Nezhad; Vahideh Nourani

Volume 15, Issue 3 , June 2026, Pages 281-304

https://doi.org/10.22056/ijir.2026.03.06

Abstract

BACKGROUND AND OBJECTIVES: The insurance industry has recently undergone profound and disruptive changes under the sweeping influence of digital transformation. The emergence of new technologies such as AI, blockchain, etc.… evolving customer expectations and intensifying competitive pressures ... Read More BACKGROUND AND OBJECTIVES: The insurance industry has recently undergone profound and disruptive changes under the sweeping influence of digital transformation. The emergence of new technologies such as AI, blockchain, etc.… evolving customer expectations and intensifying competitive pressures have driven traditional insurance companies toward adopting innovative business models, particularly platform-based designs. Among these, co‑opetition—strategic collaboration with competitors—has emerged as an effective approach for achieving sustainable competitive advantage and creating shared value. Nevertheless, only a limited number of specialized studies have explored this approach in the context of the insurance industry in depth. In fact, co‑opetition in the insurance sector has received little attention in the existing literature and in Iran almost no research has been conducted in this field. Specifically, no studies were found addressing co‑opetition within the platform ecosystems of the insurance industry. The present study aims to bridge this research gap by conducting a systematic review to extract certain themes relevant to co‑opetition strategy from related and adjacent industries and to propose a model for adapting these themes to the platform ecosystem of the insurance sector.METHODS: This research was conducted using a systematic literature review to examine the applicability of successful co‑opetition patterns from other industries in the context of insurance. To this end, the main co‑opetition themes in various industries were identified and analyzed and their potential transferability to platform industries and ecosystems—particularly the insurance sector—was assessed. The reporting process followed the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‑Analyses), which, by standardizing the reporting process, help authors to clearly and completely present the rationale for conducting the review, the methods employed, and the resulting findings. In this study, a total of 140 sources were initially identified and after two stages of screening 27 articles were selected for systematic review. The patterns found in these articles were then examined through thematic analysis, and ultimately a model for adaptation to the insurance ecosystem was presented.FINDINGS: This study, using a systematic review approach, identified and adapted co‑opetition patterns from platform-based, ecosystem-based, service, and financial industries, and evaluated their transferability to insurance platform ecosystems. The results of the combined review and analysis of the data revealed that the main co‑opetition themes, despite institutional and structural differences, occur across industries and can be localized for the insurance sector. Based on the findings, in the domain of co‑opetition in similar or related industries, six main categories of themes with various forms and dimensions were identified. The research findings include 175 unique codes from the systematic review which, through thematic analysis, ultimately yielded 6 main themes (comprising 17 sub‑themes): drivers (environmental pressures, organizational incentives, and objectives such as innovation and risk sharing), co‑opetition model selection (structural, dynamic, and domain‑based), mechanisms (strategic alliances, resource sharing, open platform models), tension management (inherent tensions, approaches to managing tension, leadership roles), strategic dimensions (levels of co‑opetition, adaptive strategies, value‑creation approaches), and outcomes (organizational, network, and ecosystem levels). It appears that in organizations that adopt co‑opetition strategies, such themes are consistently present, and issues related to them exist in these areas.CONCLUSION: Comparing co‑opetition patterns across various industries with those in the insurance sector shows that a significant portion of the identified themes, with adaptation and localization, are applicable in insurance platform ecosystems. However, characteristics such as the high sensitivity of insurance data, strict regulations, and the long‑term nature of contractual relationships underscore the need for precise and controlled implementation of each co‑opetition mechanism. The findings provide a foundation for developing hybrid co‑opetition models suited to the specific conditions of the insurance industry. The study’s recommendations for improving co‑opetition in insurance platforms focus on six key areas: (1) co‑opetition governance through hybrid models, standardized data‑sharing protocols, and the participation of all stakeholders; (2) shared infrastructure such as open technological platforms, a common technology core, and joint research and development projects; (3) collaborative learning and innovation through innovation centers, pilot programs, and experience‑transfer mechanisms; (4) tension management via leadership training, controlled information flows, and selective knowledge sharing; (5) value‑creation models including joint packages, profit sharing, and co‑creation of customer experiences, and (6) the role of regulators in creating incentives, developing preventive regulations, and supporting the reconfiguration of the value chain.

Economics of finance / insurance

The evolution and determinants of accepted in Tehran stock exchange insurance companies’ Profitability: A DEA-based perspective

S. Babaei; M. Rostami Mal Khalifeh; M. Heidari

Volume 13, Issue 1 , February 2024, , Pages 61-70

https://doi.org/10.22056/ijir.2024.01.05

Abstract

BACKGROUND AND OBJECTIVES: Profitability is a crucial indicator of insurance company performance, as it reflects their ability to invest and grow. Supervisors also rely on financial characteristics, including profitability, to determine the viability of insurers. The data envelopment analysis (DEA) method ... Read More

Loss adjuster in insurance

Designing an intelligent evaluation system for predicting fire insurance claims using deep learning

I. Raeesi Vanani; M. Taghavifard; B. Sohrabi; M. Amirhosseini

Volume 12, Issue 4 , August 2023, , Pages 251-264

https://doi.org/10.22056/ijir.2023.04.01

Abstract

Insurance rights

Ship-owners’ compulsory insurance for oil pollution liability based on 1969 civil liability convention and its amendments

D. Seify Ghare Yatagh; V. Hasani; M. Mehdipour

Volume 4, Issue 1 , January 2014, , Pages 162-180

https://doi.org/10.22056/ijir.2015.01.10

Abstract

The human right to having a healthy environment is considered as one of the most significant human rights mentioned in various international human rights documents. One of the threatening factors of this right is oil pollution which is resulted from oil carried by ships or their fuel. According to the ... Read More

Economics of finance / insurance

Eliminating the negative effects of inflation from the life insurance policies using the currency basket

Gh. Mahdavi; J. Alipour

Volume 3, Issue 3 , July 2014, , Pages 287-302

https://doi.org/10.22056/ijir.2014.03.03

Abstract

One of the factors of insufficient demand for life insurance in the country is the presence of stable inflation, which reduces the purchasing power of life insurance capital. To investigate this issue, 204 questionnaires were distributed among two equal groups of life insurance buyers and those who have ... Read More

Insurance Social Studies

The relationship between transformational leadership and organizational creativity and innovation in private insurance companies in Iran

N. Mazloomi; V. Nasehifar; G. Ehsan Far

Volume 2, Issue 1 , January 2013, , Pages 18-31

https://doi.org/10.22056/ijir.2013.01.02

Abstract

One of the factors influencing creativity and innovation is leadership. Transformational leaders are looking for new ways of doing work, new opportunities and empowering employees to achieve the goals of the organization. The current research was also conducted with the aim of investigating the relationship ... Read More

Original Research Paper Future research in the insurance industry

Futures Studies in Takaful Accounting in Iran

Mohammad Javad Zare Bahnamiri; Mohammad hasan Maleki; Amir Abbas Salemi

Articles in Press, Accepted Manuscript, Available Online from 25 January 2026

Abstract

BACKGROUND AND OBJECTIVES: In recent decades, the Takaful industry has experienced rapid growth in Islamic countries as a Shariah-compliant alternative to conventional and Western insurance. With the increasing demand for transparency and compliance with Shariah principles, the need for specialized accounting ... Read More BACKGROUND AND OBJECTIVES: In recent decades, the Takaful industry has experienced rapid growth in Islamic countries as a Shariah-compliant alternative to conventional and Western insurance. With the increasing demand for transparency and compliance with Shariah principles, the need for specialized accounting frameworks for this industry has become prominent. The introduction of Takaful into Iran’s formal insurance system has created multiple challenges in the areas of recognition, measurement, and financial disclosure, necessitating a futures-oriented approach.The primary objective of this research is to identify and analyze the key driving forces shaping the future of Takaful accounting in Iran, to prioritize these drivers, and to develop plausible scenarios for this future. Accordingly, the findings of this study can provide a scientific and policy-oriented roadmap for insurance companies, regulators, and professionals.METHODS: This research is applied in terms of purpose and adopts a mixed-methods approach in terms of methodology. To achieve the research objectives, a combination of two quantitative methods—the Fuzzy Delphi Method and the MARCOS multi-criteria decision-making method was employed. The number of experts participating in this study was 15, which is considered appropriate and acceptable for judgment-based, expert-centered methods. The expert evaluation questionnaire was developed based on a literature review, and to assess its validity, the Content Validity Ratio (CVR) and Content Validity Index (CVI) of Lawshe were applied. The research process was conducted during 2024–2025.This study was conducted in four stages. In the first stage, drivers influencing the future of Takaful accounting were identified through a literature review. In the second stage, expert evaluation questionnaires were distributed among selected experts to screen the identified drivers using the Fuzzy Delphi Method. In the third stage, key drivers affecting the future of Takaful accounting in Iran were prioritized to extract the final drivers via the MARCOS multi-criteria decision-making method. In the fourth stage, plausible Takaful accounting scenarios in Iran were mapped using the final drivers, with focus group interviews as the primary tool.FINDINGS: In the first stage, an initial list of drivers influencing the future of Takaful accounting in Iran was prepared through a review of previous studies. In this stage, 25 drivers were identified and classified into five main categories: socio-cultural, technological, regulatory, market-related, and economic drivers. In the second stage, the Fuzzy Delphi Method was employed to screen these drivers and identify the key ones. For this purpose, a questionnaire was designed and distributed among a group of experts. As a result of this process, 17 drivers were excluded due to not meeting the required criteria, and 8 drivers were selected for final ranking. The Content Validity Ratio (CVR) and Content Validity Index (CVI) of Lawshe were calculated for the drivers in this study, confirming that all retained drivers possessed acceptable validity.In the next stage, these 8 drivers were further evaluated using the MARCOS method to rank and prioritize them based on multiple criteria. According to the scores, the drivers “competitiveness of Takaful against conventional insurance” and “development of Islamic financial culture” received the highest priority.In the final stage, future scenarios for Takaful accounting were developed based on these two high-priority drivers and focus group interviews. Each driver consisted of two contrasting states. The contrasting states for the first driver are: low competitiveness of Takaful against conventional insurance versus high competitiveness of Takaful against conventional insurance. The second driver also has two contrasting states, which are: significant development of Islamic financial culture versus weak development of Islamic financial culture.Overall, four plausible future scenarios for Takaful accounting in Iran were derived, reflecting different combinations of these two drivers, including significant development of Islamic financial culture alongside low competitiveness of Takaful, significant development of Islamic financial culture alongside high competitiveness of Takaful, weak development of Islamic financial culture alongside low competitiveness of Takaful, and weak development of Islamic financial culture alongside high competitiveness of Takaful.CONCLUSION: Considering the four plausible scenarios for the future of Takaful accounting in Iran, it is essential for policymakers and insurance companies to adopt a set of coordinated measures to enable the sustainable and competitive development of Takaful. These measures should contribute both to the expansion of Islamic financial culture and to strengthening the competitiveness of Takaful against conventional insurance. The following recommendations are structured around five key areas. Key areas for action include establishing a legal and regulatory framework for the formation of Takaful companies, providing tax incentives, formulating Shariah-based regulations, and creating specialized regulatory bodies to enhance transparency and accountability; developing diverse, simple, and competitive insurance products tailored to societal needs while standardizing schemes to increase trust; conducting public awareness campaigns, promoting Islamic financial culture, and training specialized human resources; utilizing artificial intelligence, blockchain, and data analytics to improve processes, transparency, and efficiency; and expanding Takaful reinsurance, establishing Shariah-compliant guarantee funds, and employing Islamic financial instruments within company structures.

Original Research Paper New Insurance Technologies

A Longitudinal Assessment of Big Data Maturity and Its Impact on Online Trust and Performance in Insurance Companies: A TDWI-Based Approach

leili niakan

Articles in Press, Accepted Manuscript, Available Online from 22 September 2025

Abstract

BACKGROUND AND OBJECTIVES: The insurance industry, as a critical pillar of national economic infrastructure, is increasingly data-driven, facing the challenge of managing and utilizing vast volumes of both structured and unstructured data. With the exponential growth of digital operations, the ability ... Read More BACKGROUND AND OBJECTIVES: The insurance industry, as a critical pillar of national economic infrastructure, is increasingly data-driven, facing the challenge of managing and utilizing vast volumes of both structured and unstructured data. With the exponential growth of digital operations, the ability to effectively harness big data has become essential for enhancing organizational performance, improving customer experiences, and gaining competitive advantage. This study focuses on evaluating the maturity of big data implementation in the Iranian insurance sector over an extended period, identifying key progress areas, existing challenges, and practical pathways to advancement.METHODS: To this end, a longitudinal, descriptive-survey research design was employed. The research was conducted in two distinct time points—2016 and 2024—across a sample of 25 Iranian insurance companies. The study adopted the TDWI Big Data Maturity Model, selected due to its comprehensive framework, industry validation, and clarity in categorizing organizational maturity into five levels: nascent, pre-adoption, early adoption, corporate adoption, and mature/visionary. Data collection was carried out using a standardized TDWI questionnaire, administered to a statistical population comprising IT managers, domain experts, and senior personnel with in-depth knowledge of their companies’ data practices. Participants completed the questionnaire through a self-assessment process.The TDWI model used in this study incorporates multiple dimensions of big data maturity, including technical capabilities, organizational readiness, and customer-related aspects—most notably, customer online trust. Each dimension was evaluated through a range of indicators aimed at measuring implementation, integration, and strategic alignment with big data goals.FINDINGS: The findings demonstrate a clear and significant increase in big data maturity across the sector over the eight-year study period. The average maturity score among the participating companies rose from 12 in 2016—corresponding to the "nascent" stage—to 29 in 2024, placing them within the "early adoption" category. More specifically, the number of companies operating at the nascent level dropped drastically from 18 to only 2, while the number of organizations attaining higher maturity levels saw a proportional rise. This trend signals growing awareness, investment, and institutional commitment to data-driven strategies within the industry.The study also highlights the progress made in individual maturity dimensions. Indicators related to "customer online trust" experienced marked improvement in the majority of companies, suggesting that insurers have made efforts to align digital platforms and service delivery with customer expectations regarding privacy, transparency, and system reliability. However, despite overall progress, the "data management" dimension continued to lag behind others, with an average score of 25, indicating a “pre-adoption” status. This reflects a persistent challenge in establishing strong data governance frameworks, centralized data architectures, and integrated data lifecycle management practices.In terms of statistical analysis, correlation tests were conducted to explore potential relationships between big data maturity and organizational characteristics. Results revealed no significant correlation between maturity levels and company size—measured by number of employees, number of branches, or financial turnover. In contrast, significant positive correlations were found between maturity levels and two key factors: the size of the IT budget allocated and the perceived level of online trust in the company’s information systems, especially when benchmarked against peer organizations. These results suggest that investment in digital infrastructure and customer trust-building measures are more predictive of maturity advancement than traditional organizational metrics.Based on these insights, the study offers several practical recommendations. First, it underscores the need for insurance companies to develop and implement a comprehensive big data strategy aligned with business objectives and customer needs. Second, investing in the education, training, and development of specialized human resources in big data analytics is essential to sustaining long-term progress. Third, fostering cross-organizational collaboration, including knowledge-sharing and benchmarking activities among insurance firms, can accelerate collective maturity growth across the industry.CONCLUSION: This study highlights significant progress in big data maturity across the Iranian insurance industry between 2016 and 2024. While notable improvements have been achieved—particularly in customer-oriented dimensions such as online trust—core areas like data management remain underdeveloped, indicating the need for more balanced digital growth. The lack of correlation between maturity level and company size suggests that strategic vision and IT investment play a greater role than scale alone.To advance further, insurance firms should prioritize developing long-term data strategies, invest in specialized human capital, and foster industry-wide knowledge sharing. Policymakers can support this evolution through standardized frameworks and benchmarking initiatives.Overall, digital transformation through big data is well underway in the sector, but unlocking its full value requires strengthening internal capabilities, aligning organizational structures with data objectives, and cultivating a culture of innovation and trust.

Original Research Paper Insurance pricing

SUPERVISORY MODEL FOR RATE ADEQUACY OF EXCESS OF LOSS RISK AND CATASTROPHE REINSURANCE TREATY LAYERS IN REINSURANCE COMPANIES, USING A COORDINATED MECHANISM BASED ON EXPOSURE

ali Sadeghkhani; Mohsen gharahkhani

Articles in Press, Accepted Manuscript, Available Online from 24 February 2026

Abstract

BACKGROUND AND OBJECTIVES: Assessing the rate adequacy of excess-of-loss (XoL) reinsurance treaty layers, encompassing both non-catastrophe risk and catastrophe exposures, has become a critical challenge in contemporary reinsurance supervision. Increasing loss volatility, climate-driven catastrophe risk, ... Read More BACKGROUND AND OBJECTIVES: Assessing the rate adequacy of excess-of-loss (XoL) reinsurance treaty layers, encompassing both non-catastrophe risk and catastrophe exposures, has become a critical challenge in contemporary reinsurance supervision. Increasing loss volatility, climate-driven catastrophe risk, growing concentration of insured values, and heightened regulatory emphasis on internal risk-based supervision have exposed structural weaknesses in traditional XoL pricing practices. In many reinsurance markets, pricing decisions continue to rely on single-family severity distributions, historical average loss ratios, or prevailing market-leading terms. While such approaches offer operational simplicity, they frequently underestimate tail risk, ignore parameter uncertainty, and fail to reflect company-specific characteristics such as portfolio composition, exposure growth, capital constraints, and retrocession structure. These shortcomings are particularly pronounced in mid and upper XoL layers, where data scarcity and heavy-tailed loss behavior materially affect solvency outcomes.The primary objective of this study is to develop an internal supervisory framework for evaluating the rate adequacy of XoL treaty layers that moves beyond conventional pricing benchmarks and explicitly links technical rates to portfolio risk and capital resilience. Rather than treating rate adequacy as a standalone actuarial exercise, the proposed framework embeds pricing analysis within the broader context of internal risk and solvency assessment (IRSA), providing a forward-looking tool for identifying rate inadequacy and its capital implications.METHODS: The proposed framework adopts a coordinated, multi-perspective methodology that distinguishes between non-catastrophe risk layers and catastrophe layers while integrating exposure dynamics across both. For non-catastrophe risk XoL treaties, loss severity is modeled using a spliced distribution, combining a lognormal distribution for the body of losses with a generalized Pareto distribution for the tail. Model parameters are estimated using maximum likelihood techniques, and Monte Carlo simulation is applied to generate synthetic loss samples. From these simulations, key technical measures are derived, including limited expected values, layer-specific technical premiums, and technical rate-on-line benchmarks.For catastrophe XoL layers, where historical loss data are typically sparse, a Bayesian frequency–severity modeling approach is employed. Industry-informed prior distributions are combined with limited company-specific experience to obtain posterior distributions that explicitly capture parameter uncertainty. This structure enables the estimation of predictive loss distributions, exceedance probabilities, and credibility intervals relevant for solvency-oriented decision-making.A key innovation of the framework is the integration of an exposure-based supervisory module. Recognizing that premium growth is an unreliable proxy for tail risk, interval-valued data are used to represent the evolution of high-value insured exposures in the tail of the portfolio. Exposure growth is translated into scaling factors that adjust loss frequency and tail behavior, ensuring consistency between pricing outcomes and the underlying risk profile. In addition, a marginal participation mechanism is introduced to quantify the incremental contribution of accepting specific layers or participation shares to aggregate portfolio risk and capital consumption.FINDINGS: The framework is illustrated through a case study of a multi-layer property risk XoL treaty implemented in the domestic reinsurance market. The results demonstrate that conventional pricing approaches based on single-family severity distributions materially understate tail losses, particularly in mid and upper layers. While the first layer is broadly aligned with its internally derived technical rate, several higher layers exhibit substantial rate inadequacy relative to technical benchmarks. The spliced severity model produces significantly higher and more realistic technical rates for layers exposed to heavy-tailed loss behavior, correcting the optimistic bias observed under lognormal-only specifications.The Bayesian catastrophe module yields stable parameter estimates and explicit uncertainty measures, highlighting the range of plausible loss outcomes relevant for internal solvency assessment. Exposure-based analysis further reveals that observed premium growth fails to capture the true expansion of tail exposure. The marginal participation analysis shows that the existing layer structure constrains the reinsurer’s ability to accept homogeneous participation shares without disproportionately increasing capital strain, causing rate inadequacy in upper layers to translate directly into elevated capital consumption.CONCLUSION: This study demonstrates that rate adequacy and capital resilience are inseparable dimensions of reinsurance risk management. The proposed internal supervisory framework integrates stochastic pricing, heavy-tailed loss modeling, Bayesian uncertainty quantification, and exposure dynamics within a single analytical structure. By embedding rate adequacy assessment within the IRSA process, the framework enables reinsurers and supervisors to move beyond static market benchmarks toward risk-sensitive, forward-looking decision-making. Deviations between technical rates and market terms are translated into actionable supervisory signals, supporting informed decisions on participation shares, coverage limits, treaty acceptance, and portfolio rebalancing.

Original Research Paper Marketing and Sales

Understanding the Branding Process in the Iranian Insurance Industry Using the Glaserian Approach

Peyman Sedaghat; Ehsan Abedi; Hamidreza Yazdani; saeed sehat

Articles in Press, Accepted Manuscript, Available Online from 24 February 2026

Abstract

BACKGROUND AND OBJECTIVES: In recent years, branding in service industries, including the insurance sector, has gained growing attention as a strategic instrument for achieving competitive differentiation, enhancing public trust, and improving overall organizational performance. Unlike tangible products, ... Read More BACKGROUND AND OBJECTIVES: In recent years, branding in service industries, including the insurance sector, has gained growing attention as a strategic instrument for achieving competitive differentiation, enhancing public trust, and improving overall organizational performance. Unlike tangible products, services—and particularly insurance—are highly intangible, complex, and often subject to uncertainty, which makes branding even more essential as a means of reducing customers’ perceived risks and increasing their confidence in service providers. In the context of Iran, where the penetration rate of insurance remains considerably lower than the global average, the importance of branding becomes more pronounced. Despite the growing recognition of branding in management literature, the insurance industry in Iran still lacks a comprehensive and localized conceptual framework that captures the unique economic, regulatory, and socio-cultural complexities of this sector. Existing approaches often remain superficial, focusing mainly on advertising or fragmented marketing campaigns, rather than addressing the deeper organizational, cultural, and institutional dimensions of branding. Hence, there is a pressing need for a holistic, theory-driven, and context-specific model to explain the branding process in the Iranian insurance industry. The main aim of this study is to design and articulate such a conceptual model by applying a qualitative approach and the grounded theory methodology, thereby filling a critical gap in both academic research and industry practice.METHODS: To achieve this objective, the research employed a qualitative approach based on grounded theory, specifically following Glaser’s emergent methodology. Data were collected through semi-structured, in-depth interviews with 16 experts and practitioners in the insurance industry, including senior managers, marketing executives, brand consultants, policymakers, and researchers. Participants were selected through purposive and snowball sampling to ensure maximum relevance and diversity of perspectives. Interviews continued until theoretical saturation was reached, meaning no new themes or concepts were emerging from the data. The collected data were coded and analyzed using MAXQDA software in three systematic stages: open coding, axial coding, and selective coding. This iterative process allowed the researchers to move from raw interview transcripts to conceptual categories and finally to the construction of an integrative theoretical model. The grounded theory approach was considered appropriate because it allows for the generation of theory directly from empirical data, ensuring that the resulting framework is firmly rooted in the lived experiences of industry stakeholders rather than being imposed from existing but potentially irrelevant theories.FINDINGS: The analysis resulted in the identification of 175 final codes during the open coding stage. These codes were further synthesized and grouped into 52 main concepts, 20 subcategories, and ultimately 6 core categories. The findings revealed that branding in the insurance industry is shaped by a complex interplay of causal, intervening, and contextual conditions. Causal conditions include factors such as shifts in customer behavior, growing distrust toward insurance companies due to negative past experiences, macroeconomic instability, and the absence of an overarching branding framework at the industry level. Intervening conditions highlight the enabling or constraining role of organizational resources, particularly financial and technological capacities, managerial support, and the internal capabilities of insurance firms. Contextual conditions emphasize the broader institutional and cultural environment, such as restrictive regulations, inconsistent government policies, structural barriers within firms, and weak societal awareness of insurance as a value-creating mechanism. CONCLUSION: The results indicate that adopting appropriate strategies in insurance branding can lead to positive economic outcomes such as increasing insurance penetration rates, boosting the share of insurance in the gross domestic product, and ensuring organizational sustainability. On the socio-cultural side, the findings highlight the creation of competitive differentiation, reduced price sensitivity, and the transformation of the brand into a trusted market reference. Overall, this research shows that branding in the insurance industry is a multidimensional and interdisciplinary process, shaped by environmental and organizational factors, and plays a pivotal role in enhancing the position of insurance companies and strengthening the role of insurance in the national economy. It is recommended that insurance managers enhance their brand positioning by establishing specialized branding units, fostering a participatory organizational culture, and leveraging modern technologies (CRM, data mining, and digital marketing). Among the limitations of this study were difficulties in accessing certain key experts and challenges in analyzing the highly volatile insurance market environment in Iran. Moreover, the study’s focus on the insurance industry may restrict the generalizability of its findings to other service sectors. Future research is suggested to adopt a comparative approach across different service industries or to conduct broader field studies in order to develop or validate similar models.

Original Research Paper Economics of finance / insurance

Capital requirements for non-life insurance of Iran Insurance Company using the copula model

elham alizadeh; Jafar Haghighat; Hossein asgharpur; Zahra Karimi Takanlu

Articles in Press, Accepted Manuscript, Available Online from 12 April 2026

Abstract

BACKGROUND AND OBJECTIVES: The deviation between risk capital under standard models and its actual amount can pose multiple challenges for insurers. Although the objective of regulatory requirements is to limit insurer risk, these regulations, due to their general nature, are not always applicable to ... Read More BACKGROUND AND OBJECTIVES: The deviation between risk capital under standard models and its actual amount can pose multiple challenges for insurers. Although the objective of regulatory requirements is to limit insurer risk, these regulations, due to their general nature, are not always applicable to the specific conditions of each company. The standard formula usually operates conservatively and obligates insurers to hold more risk capital than the actual need, which reduces the potential return on equity. Therefore, improving the method of calculating the required capital beyond the standard formula to better describe the effect of risk diversification is essential. In other words, designing alternative approaches and quantifying potential deviations from regulatory standard models should be of concern to insurance managers, legislators, and public policymakers. The aim of this article is to provide a structured model for determining the capital requirements of non-life insurance companies using monthly claims data. To this end, an alternative modeling approach for insurance risk aggregation is proposed, considering two main aspects: the neglect of nonlinear dependencies and company-specific risk parameters. In fact, this model empirically demonstrates the existing gap between the required capital under standard models and capital calculated using alternative approaches. According to this, first, the marginal distribution of each line of business in the insurance portfolio must be parametrically specified to reflect the distributional characteristics of each risk factor. Then, the estimated marginal distributions are aggregated considering the dependency structures between risk factors. It is important to note that the presented modeling approach can be interpreted as a sensitivity analysis of the regulatory framework for capital calculation addressing two often-raised criticisms.METHODS: To calculate insurance risk, which is the main risk factor in insurance companies, the actual portfolio of the insurance company was considered. To avoid computational complexity, among non-life insurance lines, those with the largest share of premium in the year 1402 were used, namely: third-party liability, Car Body, accident, liability, and fire. The data include monthly paid claims for five lines in the actual portfolio of Iran Insurance Company from 1395:1 to 1402:8. To estimate the marginal copula functions under the individual risk model, various parametric distributions commonly used in actuarial and operational risk fields were fitted. The appropriate distribution for each claims process was determined based on goodness-of-fit tests and the Akaike information criterion. Dependencies between lines were modeled using different copula functions. Three statistical criteria, including the log-likelihood function, Akaike information criterion, and Bayesian Schwarz criterion, were used to select the best-fitting model among the estimated copulas. Then, based on the selected copula function and its corresponding dependency structure, n pairs of data were simulated. The simulated variables were aggregated to establish the total loss distribution under the multivariate copula. Finally, in the alternative model, the economic capital was calculated using the Value-at-Risk risk measure.FINDINGS: The analysis of the statistical properties of the data used shows that all insurance lines exhibit right skewness and greater kurtosis compared to the normal distribution, indicating the presence of heavy tails in the examined time series. Therefore, the normal distribution is not a suitable distribution for modeling insurance risk. The parameter estimation results of theoretical distributions indicate that the log-normal distribution is the best fit for the historical claim data in four lines: accident, fire, Car Body, and third-party liability, while the generalized Pareto distribution is the best fit for the liability insurance line. It is observed that the most appropriate copula function, based on the highest maximum likelihood value and the lowest information criteria, for interpreting dependency in the insurance portfolio, is the R-Vine copula, which outperforms other copula functions. The size of insurance risk based on real data for the year 1402, according to the risk coefficients in Regulatory Directive No. 69, is approximately 265,235,960 trillion IRR. In the alternative model, which is a combined model of the R-Vine copula with using the Value-at-Risk measure, the results show that the standard model estimates the required insurance risk capital to be on average 25.6% higher than the alternative model at various confidence levels.CONCLUSION: The overestimation of capital requirements identified in standard models suggests that insurers can significantly reduce their risk capital by taking into account nonlinear dependencies and their own firm-specific risk parameters. Hence, based on empirical observations, it is recommended that regulators incorporate dependency assumptions to reduce discrepancies between the risk capital derived from the standard model and its actual magnitude.

Future research in the insurance industry

Identification and Explanation of Factors Affecting Financial Resilience in the Insurance Industry in Response to Environmental Drivers

Mustafa Esmaeilnia mansour; norooz Noorolah zadeh; Seyede Mahboubeh Jafari; Shahram Chehar Mahali

Articles in Press, Accepted Manuscript, Available Online from 21 March 2026

Abstract

BACKGROUND AND OBJECTIVES: In today’s fast-paced, interconnected, and highly volatile global environment, the insurance industry stands as one of the fundamental pillars underpinning the financial and economic stability of nations. As a critical component of the broader financial ecosystem, insurance ... Read More BACKGROUND AND OBJECTIVES: In today’s fast-paced, interconnected, and highly volatile global environment, the insurance industry stands as one of the fundamental pillars underpinning the financial and economic stability of nations. As a critical component of the broader financial ecosystem, insurance institutions play a pivotal role in risk mitigation, capital mobilization, and long-term financial planning for both individuals and businesses. However, this industry is increasingly exposed to a wide array of dynamic and interrelated environmental stimuli that challenge its operational stability and long-term viability.These stimuli encompass a broad spectrum of external pressures, including persistent economic fluctuations such as inflationary trends, interest rate volatility, and global financial crises. Additionally, the escalating impacts of climate change—manifested in the form of more frequent and severe natural disasters—pose significant underwriting and actuarial challenges. Technological advancements, including the rise of digital platforms, big data analytics, and artificial intelligence, are rapidly reshaping customer expectations, operational processes, and competitive dynamics within the industry. Social factors, such as demographic shifts, evolving consumer behaviors, and changing regulatory frameworks, further compound the complexity of the environment in which insurance firms operate.METHODS: In the initial phase of the research, a qualitative approach was adopted to gain a deep and nuanced understanding of the factors influencing financial sustainability in the insurance industry. Specifically, thematic analysis was employed as the primary method for data interpretation. To this end, a series of in-depth, semi-structured interviews were conducted with a purposive sample of ten experts and senior managers operating within various segments of the insurance sector. These participants were selected based on their extensive experience, strategic insight, and familiarity with the evolving dynamics of the industry. The qualitative data gathered from these interviews were meticulously coded and analyzed using a multi-layered thematic analysis framework. This process involved identifying and categorizing emergent patterns and insights into three levels of themes: basic themes, which reflect specific observable issues; organizing themes, which group related basic themes into broader categories; and global (or overarching) themes, which encapsulate the core dimensions underlying the phenomenon under investigation.In the second phase of the study, a mixed-method validation strategy was implemented to enhance the reliability and robustness of the findings derived from the thematic analysis. For this purpose, the fuzzy Delphi method—a structured, iterative technique that integrates expert consensus with the flexibility of fuzzy logic—was applied. This method allowed for the systematic validation and prioritization of the previously identified themes by incorporating the subjective judgments of a panel of experts, while accounting for uncertainty and ambiguity in their responses. Through successive rounds of controlled feedback, the panel reached a consensus on the relative importance and relevance of each theme, thereby refining the thematic framework and ensuring its practical applicability to real-world insurance contexts.FINDINGS: The findings of the study revealed a comprehensive framework of factors that significantly contribute to the financial sustainability of insurance companies in the face of environmental volatility. At the highest level of abstraction, three overarching themes were identified as fundamental pillars influencing long-term financial stability: financial crisis management, innovation within the insurance industry, and organizational resilience. These themes collectively represent the strategic and structural capacities of insurance firms to absorb shocks, adapt to rapidly changing conditions, and maintain continuity in their financial operations. Financial crisis management emerged as a crucial component, emphasizing the ability of insurance companies to develop robust contingency plans, manage liquidity under stress, and implement risk mitigation strategies that enable survival during periods of macroeconomic or sector-specific downturns. Innovation in the insurance industry was identified as a second key theme, highlighting the importance of technological advancement, product diversification, process improvement, and the adoption of digital tools to remain competitive and responsive to changing market demands. The third overarching theme, organizational resilience, captured the internal capabilities of insurance firms to dynamically respond to disruptions—whether operational, regulatory, or environmental—through adaptive leadership, strategic agility, and a resilient corporate culture.CONCLUSION: Comparison with previous research reveals that integrating a data-driven approach, embracing technological innovation, and adopting a systematic and proactive perspective on crisis management collectively establish a forward-looking trajectory for strengthening the financial sustainability of the insurance industry. These elements enable organizations to anticipate potential risks, optimize decision-making processes, and respond to uncertainties with greater agility and precision. The findings of this study contribute not only to the theoretical understanding of financial resilience but also offer a set of actionable and context-sensitive recommendations. These insights can serve as valuable guidance for insurance managers seeking to reinforce organizational robustness, for policymakers aiming to craft more adaptive regulatory frameworks, and for academic researchers pursuing further inquiry into resilient financial systems within the insurance sector.

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Original Research Paper New Insurance Technologies

From Legislation to Underwriting: A Regulatory Analysis of the Insurability of Artificial Intelligence and the Internet of Things in the European Union

Fatemeh Najibzadeh Vamegh Abadi; Sayyed Mohammad Hadi Ghabooli Dorafshan; Hosein Naseri Moghaddam

Articles in Press, Accepted Manuscript, Available Online from 11 May 2026

Abstract

BACKGROUND AND OBJECTIVES: The rapid deployment of artificial intelligence and the Internet of Things has reshaped the allocation of liability and the logic of insurability. Performance depends on data quality and real world operating conditions. Fault attribution among multiple actors in the chain including ... Read More BACKGROUND AND OBJECTIVES: The rapid deployment of artificial intelligence and the Internet of Things has reshaped the allocation of liability and the logic of insurability. Performance depends on data quality and real world operating conditions. Fault attribution among multiple actors in the chain including manufacturers, service providers, and users is difficult. Divergent national interpretations of emerging rules increase legal uncertainty and complicate risk assessment. In response, new EU frameworks, notably the Artificial Intelligence Act and the revised Product Liability regime, introduce clear definitions, evidentiary presumptions on defect and causation, and compliance duties for high risk systems. These measures can improve predictability and market discipline, although multi level implementation and interpretive variation may limit effectiveness. Against this backdrop, the study explains how these instruments affect the insurability of losses caused by intelligent systems. It shows how definitions and liability presumptions can be translated into operational metrics for underwriting, pricing, and limits of indemnity. It examines how data governance and system transparency strengthen auditability and fault attribution. It also identifies how interpretive and implementation heterogeneity constrains cover design, aggregation control, and the development of innovative insurance products.METHODS: The study uses a legislative and analytical approach across three layers. First, it conducts a normative and interpretive reading of EU texts with emphasis on the legal definition of an AI system, the classification of risk levels, compliance duties, data governance, and the obligations of providers and users. Second, it maps these duties to technical components of insurance practice, including the management of adverse selection and moral hazard, auditability of data and models, allocation of fault, and the calibrated adjustment of deductibles and limits of liability. Third, it assesses consistency between the regulations and classic insurability criteria and develops operational scenarios for related lines of business such as technology professional liability, algorithmic product liability, and business interruption arising from failures of intelligent systems. The analysis relies on desk research and regulatory documents and uses legal analogy and law and economics reasoning.FINDINGS: Clear definitions and compliance duties, especially for high risk systems, improve behavioral and data auditability. Presumptions on defect and causation reduce evidentiary costs and increase the predictability of liability. This enables risk based pricing and the design of contractual provisions that set information duties and help organize secondary data markets. Nonetheless, the risk based approach in the AI Act still struggles to differentiate levels and types of risk with sufficient precision. Heterogeneity in classification and the influence of non technical considerations can obscure correlations among losses and raise the likelihood of rare but severe events. In practice this increases the need for prudential capital and the use of aggregation limits in policies. Although presumptions that ease the burden of proof can streamline litigation, differences in national interpretation amplify legal heterogeneity and litigation risk, which in turn raise precautionary premium rates and broaden exclusions. The absence of fully harmonized enforcement at EU level heightens the risk of divergent standards, increases multi jurisdiction compliance costs, and complicates the harmonization of general conditions of insurance. By contrast, effective implementation of data governance duties including data quality, traceability, event logging, and full model documentation creates the basis for measurable algorithmic risks and supports risk management tools such as appropriate insurance reinsurance arrangements and performance based policy provisions. These measures enhance the capacity of insurers to assess and accept risk.CONCLUSION: Insurance remains effective in the face of AI and IoT risks when three conditions are met. First, traceability and auditability of models and data must be translated into pre loss and post loss contractual duties. Second, risk classification must move from symbolic labels to operational measurability that uses indicators of data quality, model stability, explainability, and error rates. Third, interpretive convergence across the EU must be reinforced through governance tools that include binding guidance, a technical legal arbitration body, and minimum standard templates for contractual conditions of insurance. Otherwise, emerging risks such as error propagation across ecosystem chains and synchronous shocks generated by model updates will destabilize premium rates and constrain market capacity. The paper recommends periodic regulatory review using insurability metrics, standard templates for disclosure and event logging duties, specialist training for adjusters who handle algorithmic losses, and the development of reinsurance frameworks for accumulative risks at EU scale.

Original Research Paper Future research in the insurance industry

the impact of economic policy uncertainty and sanctions on insurance penetration in Iran

Bakhtiar Javaheri; Saman Ghaderi; Zanko Ghorbani

Articles in Press, Accepted Manuscript, Available Online from 18 May 2026

Abstract

BACKGROUND AND OBJECTIVES : The insurance industry, as one of the influential financial institutions in the economies of countries, acts as a risk management tool and provides a security cover for assets, health, and life of individuals, bringing financial stability to households and firms. By pooling ... Read More BACKGROUND AND OBJECTIVES : The insurance industry, as one of the influential financial institutions in the economies of countries, acts as a risk management tool and provides a security cover for assets, health, and life of individuals, bringing financial stability to households and firms. By pooling financial resources in the form of insurance premiums and directing them towards large-scale investments, the insurance industry serves as a driver of economic growth and a supplier of long-term capital. In countries like Iran, which are constantly exposed to systematic risks, uncertainties, and domestic and foreign crises, the role of insurance in creating security and stability becomes more prominent. Among the most important factors creating uncertainty in Iran's economy in recent decades are fluctuations in domestic economic policies (economic policy uncertainty) and international economic sanctions. These two phenomena, by creating instability in macroeconomic variables, can influence the behavior of consumers and investors, including the demand for insurance services. The life insurance market in Iran has historically remained underdeveloped, accounting for only about 10-14% of total insurance premiums, due to chronic inflation, underdeveloped capital markets, and cultural factors. Therefore, the main objective of this study is to analyze and quantify the impact of economic policy uncertainty and economic sanctions on the development of the insurance industry in Iran. For this purpose, the insurance penetration rate (the ratio of total insurance premiums to GDP) is considered as an indicator of insurance development, and the responsiveness of its two important sectors, namely life insurance and non-life insurance, has been examined separately in two distinct models over the period from 1989 to 2023, using the Dynamic Ordinary Least Squares (DOLS) econometric method. The key innovations of this study are: separately analyzing life and non-life insurance penetration; quantifying sanctions as an independent variable alongside economic policy uncertainty; employing the DOLS method which provides more efficient estimates; and comparing the magnitude of coefficients across the two sectors.METHODS: The Dynamic Ordinary Least Squares (DOLS) method was employed to estimate the long-run relationships among variables over 1989–2023. DOLS incorporates leads and lags of explanatory variables, providing efficient and unbiased cointegration estimates while reducing endogeneity. This approach allows a mix of I(0) and I(1) variables. The main independent variables are economic policy uncertainty (EPU) and economic sanctions, with control variables including trade openness, inflation (INF), foreign direct investment, industrialization, and oil price uncertainty (OIL). The sanctions index is a fuzzy logic-based composite measure, while EPU and OIL indices were sourced from the Policy Uncertainty website. ADF unit root tests showed that EPU, INF, and OIL are I(0), whereas insurance penetration rates (life and non-life), sanctions, trade openness, FDI, and industrialization are I(1). The Johansen-Juselius cointegration test confirmed long-run relationships in both models. Hansen's instability test verified the stability of these relationships, and the Jarque-Bera test confirmed residual normality.FINDINGS: The results indicate that although the direction of impact is consistent in both models, the magnitude of coefficients differs significantly. Economic policy uncertainty has a negative and significant effect on the penetration rate of both types of insurance. However, the intensity of this effect on non-life insurance, with a coefficient of -2.62, is substantially greater than its effect on life insurance (-0.43). Economic sanctions have a positive and significant impact on both sectors. This positive effect is also more pronounced for non-life insurance (2.26) compared to life insurance (0.58). Furthermore, trade openness, inflation rate, and foreign direct investment positively affect both insurance sectors. A one-unit increase in trade openness increases life and non-life penetration by 0.05 and 0.54 units, respectively. A one-unit increase in inflation increases life and non-life penetration by 0.04 and 0.21 units, respectively. A one-unit increase in FDI increases life and non-life penetration by 0.03 and 0.17 units, respectively. On the other hand, the industrialization index and oil price uncertainty have a negative impact. A one-unit increase in industrialization reduces life and non-life penetration by 0.02 and 0.88 units, respectively. A one-unit increase in oil price uncertainty reduces life and non-life penetration by 0.17 and 0.44 units, respectively. The main difference lies in the lower market share of life insurance compared to non-life insurance in Iran, which has resulted in the impact coefficients generally being larger in the non-life sector.CONCLUSION: The research results indicate that domestic policy uncertainty leads to a decrease in macroeconomic stability and a reduction in people's purchasing power. Economic policy uncertainty disrupts business planning, postpones investment decisions, and increases transaction costs for insurers, ultimately reducing demand for insurance products. Furthermore, when economic sanctions are imposed on a target country in the long run, they create a form of forced adaptation and necessitate management of new risks, which ultimately strengthens the demand for insurance services. Sanctions increase operational risks such as supply chain disruptions, exchange rate volatility, and bankruptcy probabilities, forcing firms and households to turn to domestic insurance as the last available defensive shield. This positive effect reflects forced import substitution in insurance services. The weaker response of life insurance compared to non-life insurance is attributed to its small market share in Iran, the prevalence of parallel saving instruments (gold, foreign exchange, housing), and chronic inflation. Therefore, policymakers should strive towards stabilizing the macroeconomy, encouraging the growth of insurance demand, and designing insurance products under conditions of economic fluctuations and shocks such as sanctions.

Original Research Paper Risk management in the insurance industry

The reinsurance cession efficient frontier of Iranian Insurers

Majid Benvidi; seyyed mohammadreza Miri Lavasani; ghodratollah emamverdi; Kambiz Peykarjou

Articles in Press, Accepted Manuscript, Available Online from 14 July 2026

Abstract

BACKGROUND and OBJECTIVES: The capacity to retain underwriting risk and the continuous provision of strategic mechanisms to augment this capacity are fundamentally essential to the operational stability, long-term survival, and financial resilience of insurance institutions. A primary and globally recognized ... Read More BACKGROUND and OBJECTIVES: The capacity to retain underwriting risk and the continuous provision of strategic mechanisms to augment this capacity are fundamentally essential to the operational stability, long-term survival, and financial resilience of insurance institutions. A primary and globally recognized strategy to achieve this structural resilience is executing robust risk transfer policies through ceded (outward) reinsurance operations. Next to an insurer’s intrinsic capital base and shareholders’ equity, ceded reinsurance serves as the predominant and most accessible instrument for creating additional underwriting capacity, elevating solvency margins, and hedging against catastrophic, cumulative, or unexpectedly heavy losses. However, the financial costs associated with purchasing these reinsurance protections—alongside the strategic necessity of ceding a significant portion of potential insurance benefits and premium income to reinsurers to secure their participation in potential losses—constitute a major operational expenditure for direct insurers. This issue is critically important given the specific characteristics, macroeconomic fluctuations, and unique dynamics of the Iranian insurance market, which often operates under constrained external capacities and requires high internal optimization. Consequently, maximizing the productivity of these consumptive expenses to secure optimal returns, minimizing unnecessary premium leakage, and formulating an efficient ceded reinsurance strategy have become vital for the competitive success of insurers. Therefore, this study aims to determine the ceded reinsurance efficiency frontier of Iranian insurers in a rigorous comparative framework over a specified period, directly addressing existing ambiguities regarding the mathematical and practical optimization of outward reinsurance operations.METHODOLOGY: This research employs a rigorous quantitative approach utilizing advanced Data Envelopment Analysis (DEA) techniques based on critical financial ratios directly linked to cede reinsurance operations. The mathematical model systematically evaluates the operational and technical efficiency of 22 direct Iranian insurers, acting as autonomous Decision-Making Units (DMUs), over four consecutive financial years (spanning 1400 to 1403). The DEA framework utilized incorporates both constant and variable returns to scale assumptions to provide a nuanced understanding of scale efficiencies within the sector. By identifying the fully efficient units that maximize their output-to-input ratios, the optimal ceded reinsurance efficiency frontier is precisely delineated for each respective financial year. Furthermore, the highly fluctuating macroeconomic and market conditions during this specific four-year timeframe necessitated a granular, longitudinal comparative analysis to discover the underlying reasons for shifts within the efficiency frontier over time. Consequently, the operational outcomes and productivity trajectories of each year were comprehensively evaluated and analyzed utilizing the Malmquist Productivity Index (MPI). By meticulously tracking temporal changes in overall efficiency and isolating the contributing factors—specifically distinguishing between technical efficiency changes (the catch-up effect) and technological changes (the frontier shift)—the MPI plays a pivotal role in developing targeted, data-driven strategies to continuously enhance the operational performance of inefficient DMUs.FINDINGS: Following the precise determination of the mathematical efficiency frontier based on the performance of the fully efficient units of each evaluated year, targeted strategic pathways and corrective optimization solutions for efficiency enhancement were designed and prescribed using the calculated λ (lambda) values. These solutions are presented to establish empirically validated and highly specific benchmarking frameworks for insurance institutions diagnosed with sub-optimal or inefficient outward reinsurance operations. The reference weights, denoted as λ, precisely indicate which peer companies should be emulated to reach the optimal frontier. Additionally, the longitudinal analysis facilitated by the MPI across the evaluated financial periods demonstrated a highly dynamic and frequently shifting efficiency frontier. This observable dynamism is directly attributed to the evolving systemic conditions of the national insurance market, including inflation rates, regulatory adjustments, and variations in domestic retention capacities during the 1400 to 1403 period. The decomposition of the Malmquist index revealed that while some insurers improved their internal processes, the overall industry frontier experienced periods of regression and progression due to external shocks. These dynamic results, synthesized with the customized benchmarking strategies, culminate in the formulation of comprehensive macro-level action plans that can be practically utilized by executives.CONCLUSION: The outcomes of this study successfully identify and elucidate the optimal ceded reinsurance efficiency frontier for Iranian insurers, while explicitly outlining the strategic interventions required by direct insurers to structurally improve the functional efficiency of these critical risk-transfer operations. In this regard, localized, highly actionable performance improvement strategies have been explicitly extracted based on the λ reference weights derived from the optimally performing benchmark units. Furthermore, the study calculates and presents auxiliary slack variables which precisely quantify the mathematical deficit or surplus within the input and output financial ratios. This slack analysis highlights exact, quantified areas for operational adjustment, such as reducing unnecessary ceded premiums or renegotiating for better commission rates. Finally, the comprehensive analysis of the MPI, carefully contextualized against the specific economic characteristics and systemic challenges of each financial period, provides a robust, evidence-based, and highly functional analytical foundation. This foundation is designed to directly guide the strategic decision-making processes of corporate insurers and enhance the supervisory frameworks of national regulatory bodies, ensuring long-term market stability.

Original Research Paper Insurance pricing

A Climate-Informed Actuarial Pricing Framework for Catastrophe Excess of Loss Reinsurance: A Case Study of Flood Risk in Iran

Ali Zinsaz; Omid Fathi

Articles in Press, Accepted Manuscript, Available Online from 14 July 2026

Abstract

Background and Objectives: The increasing frequency and severity of floods in Iran, coupled with accelerating climate change, has become a serious challenge to the stability of insurance and reinsurance markets. Iran, due to its geographical location and climatic diversity, has historically been exposed ... Read More Background and Objectives: The increasing frequency and severity of floods in Iran, coupled with accelerating climate change, has become a serious challenge to the stability of insurance and reinsurance markets. Iran, due to its geographical location and climatic diversity, has historically been exposed to natural disasters, with floods accounting for over 60% of total economic losses from natural catastrophes between 1990 and 2023. Major recent events, such as the devastating floods of March 2019, which caused more than $8 billion in economic damage, have starkly revealed the country's high vulnerability and the urgent need for efficient risk transfer mechanisms. Under such circumstances, sole reliance on traditional actuarial pricing models based on stationary historical data—an approach that assumes the past is a reliable guide to the future—can lead to systematic underestimation of future risks. This limitation is particularly critical for the upper layers of catastrophe excess-of-loss (Cat XL) reinsurance, which are inherently exposed to extreme tail risks where data is scarce but potential losses are catastrophic. The scientific consensus confirms that the climate system is undergoing fundamental changes due to anthropogenic activities, rendering the stationarity assumption increasingly untenable for weather-related risks. This study aims to design and apply a novel climate-informed actuarial pricing framework to quantify the impact of climate change on the technical pricing of flood reinsurance in Iran. The main focus is on estimating and analyzing the "climate risk premium" across different reinsurance layers, thereby providing a quantitative basis for incorporating climate projections into actuarial practice.METHODS: To model annual flood losses over the period 1990–2023, a composite Lognormal–Generalized Pareto Distribution (GPD) was employed to adequately capture the heavy-tailed behavior of the data. To incorporate climate change effects, projected changes in the extreme precipitation index (RX5day) from regional climate models under SSP2-4.5 and SSP5-8.5 scenarios were extracted and used to adjust the scale parameter of the GPD. This transformation shifted the loss distribution from a stationary to a non-stationary framework. Subsequently, a Monte Carlo simulation with 100,000 iterations was conducted to price a four-layer catastrophe excess-of-loss reinsurance program.FINDINGS: The results demonstrate that the composite Lognormal-GPD distribution provides a statistically superior fit to the tail of historical loss data compared to conventional single-distribution approaches, as confirmed by Kolmogorov-Smirnov tests (p-value > 0.05) and Q-Q plot analysis. Analysis of the regional climate model outputs revealed mean relative increases in the RX5day index for Iran of approximately +12% under SSP2-4.5 and +24% under SSP5-8.5. These projected changes translated into corresponding adjustments to the GPD scale parameter, shifting the loss distributions toward larger values. Under the high-emission scenario (SSP5-8.5), the "climate risk premium"—defined as the percentage increase in the technical rate relative to the stationary historical baseline—exhibited a pronounced and systematic upward gradient across reinsurance layers. Specifically, the climate risk premium was calculated at 8% for the lowest layer ($5 million excess of $5 million), increasing to 11.1% for the second layer, 19.9% for the third layer, and reaching 38% for the highest layer ($90 million excess of $60 million). Furthermore, the exhaustion probability of the top layer more than doubled, increasing from 0.00094 in the historical baseline to 0.00201 under SSP5-8.5. These findings clearly indicate that the impact of climate change is disproportionately more severe on higher and riskier layers, creating a distinct "premium gradient" that traditional stationary models cannot capture.Conclusion: Ignoring the non-stationary nature of climate risks leads to systematic and layer-dependent underpricing in catastrophe reinsurance, with the magnitude of underpricing increasing dramatically for upper layers. The proposed climate-informed actuarial framework provides a practical, transparent, and replicable methodology for primary insurers and reinsurers to directly integrate climate projections into their technical rate calculations. By quantifying the climate risk premium explicitly, this approach enables more accurate pricing, improved capital allocation, and enhanced risk management. For Iranian insurers, the findings imply an 8–38% increase in technical rates for flood catastrophe layers, with corresponding implications for pricing and reinsurance purchasing decisions. For international reinsurers, the framework offers a robust basis for evaluating flood risk in Iran under different climate scenarios and informing portfolio strategy. More broadly, this approach can enhance market resilience, support evidence-based regulatory decisions, and inform optimal risk transfer strategies in Iran and other vulnerable regions facing similar climate challenges. The transition from stationary to dynamic paradigms in catastrophe risk management is an unavoidable necessity in an era of accelerating climate change.

Original Research Paper Insurance rights

Civil Liability for Cyber Damages in Imamiyyah Jurisprudence and the Role of Insurance

Atieh Moghadamfar; Maryam seifmar

Articles in Press, Accepted Manuscript, Available Online from 14 July 2026

Abstract

Expanded AbstractBACKGROUND AND OBJECTIVES: The rapid expansion of cyberspace has given rise to unprecedented digital threats that fundamentally challenge traditional civil liability frameworks rooted in tangible interactions. Incidents such as large-scale data breaches, destructive ransomware attacks, ... Read More Expanded AbstractBACKGROUND AND OBJECTIVES: The rapid expansion of cyberspace has given rise to unprecedented digital threats that fundamentally challenge traditional civil liability frameworks rooted in tangible interactions. Incidents such as large-scale data breaches, destructive ransomware attacks, and sophisticated cyber intrusions have become pervasive, with some causing losses of hundreds of millions of dollars, underscoring the urgent need to redefine liability and compensation in the digital age. While existing research has explored cyber law, insurance, and liability separately, a critical gap remains: the absence of a systematic analysis of how the Imamiyyah jurisprudential system, specifically its doctrine of “Causes of Liability” (Mawājib al-Ḍamān), can form an indigenous foundation for civil liability and cyber insurance. This study aims to fill this gap by proposing a jurisprudentially-grounded model for a “New Civil Liability” in cyberspace, examining the extent to which Mawājib al-Ḍamān rules provide the necessary Sharia-compliant basis for determining liability and compensation for cyber harms, and how they can be integrated with cyber insurance.METHODS: This research employs a descriptive-analytical and comparative approach, systematically interpreting the foundational principles of Imamiyyah liability rules Itlāf (direct causation of loss), Tasabbub (indirect causation through negligence), Ḍamān al-Yad (liability through unauthorized possession), Ghurūr (deception), and Lā Ḍarar (prohibition of harm) and applying them to contemporary cyber incidents and insurance mechanisms. Data collection was carried out through extensive library research encompassing primary Imamiyyah jurisprudential sources spanning a study period from 1600 to 2025, from foundational 17th-century works to the most recent scholarly contributions in 2025. The analytical procedure proceeded through four interconnected stages employing deductive reasoning and systematic content analysis: first, jurisprudential analysis establishing the property status (māliyyah) of digital assets as a precondition for liability and insurance; second, identification and classification of major cyber threats and assessment of their insurability; third, direct application of each of the five core rules of Mawājib al-Ḍamān to concrete cyber harm scenarios; and fourth, jurisprudential analysis of cyber insurance grounded in the theories of naql al-dhimma (transfer of obligation) and Ḍamān al-YadFINDINGS: The study yields four principal findings demonstrating the inherent adaptability of Imamiyyah jurisprudence. First, digital assets including software, databases, and electronic records satisfy the flexible jurisprudential criterion of māliyyah (property qualification) due to their demonstrable economic value, market exchangeability, and rational desirability, rendering them valid subjects for insurance contracts and liability rules. Second, core liability rules are directly applicable to cyber harms: Itlāf covers data destruction and deprivation of access, as in ransomware encryption; Tasabbub addresses negligence in cybersecurity, holding service providers liable when their omissions enable attacks; Ḍamān al-Yad governs unauthorized access, copying, or dissemination of data as wrongful dominion over another's property; and Ghurūr provides a two-sided foundation for utmost good faith in cyber insurance, obligating full disclosure by the insured and prohibiting misleading representations by the insurer. Third, the overarching principle of Lā Ḍarar furnishes a broad basis for liability extending beyond material losses to immaterial harms including privacy violations, reputational injury, and disruption of critical services. Fourth, cyber insurance is reconceptualized as an institutionalized mechanism of naql al-dhimma, operationalizing the jurisprudential duty to compensate loss and providing solid Sharia-based legitimacy for cyber insurance as permissible risk transfer.CONCLUSION: This research articulates a coherent model for a “New Civil Liability” regime in cyberspace firmly rooted in Imamiyyah jurisprudence. It demonstrates that the classical system of Mawājib al-Ḍamān, through dynamic interpretation (ijtihād), offers a precise, flexible, and comprehensive framework for attributing responsibility and ensuring compensation in the digital realm. The model provides three interrelated contributions: a vital indigenous doctrinal basis for legislators drafting cyber liability laws; a reasoned analytical tool for the judiciary in adjudicating cyber disputes; and a practical template for designing Sharia-compliant cyber insurance products. By establishing this jurisprudential foundation, the research contributes to national cyber risk management strategies and facilitates the emergence of a legitimate, efficient, and equitable cyber insurance market in Iran. The findings underscore the enduring relevance and adaptive capacity of Imamiyyah legal heritage in addressing the most pressing challenges of the digital era, bridging the gap between classical Islamic legal scholarship and contemporary governance needs. This research constitutes a foundational and necessary step intended to catalyse further studies employing Islamic jurisprudence in the formation of just and effective legal systems that are capable of responding to emerging technological developments.

Original Research Paper Risk management in the insurance industry

Meta-models: A Novel Approach for Valuation and Risk Management of Large Variable Annuity Portfolios

Saghar Heidari; Morteza Hosseingholipour

Articles in Press, Accepted Manuscript, Available Online from 14 July 2026

Abstract

BACKGROUND AND OBJECTIVES: Variable annuities have become a popular tool in retirement planning due to their attractive guarantees. These insurance products offer a reliable, steady income stream, not only aiding in financial stability during retirement but also enhancing mental well-being and overall ... Read More BACKGROUND AND OBJECTIVES: Variable annuities have become a popular tool in retirement planning due to their attractive guarantees. These insurance products offer a reliable, steady income stream, not only aiding in financial stability during retirement but also enhancing mental well-being and overall quality of life by enhancing financial security and reducing anxiety. Variable annuities, offered by insurance companies, are tax-deferred retirement products designed to help individuals navigate the challenges of preserving the long-term value of their assets amid economic volatility, inflation, and uncertainty. However, the risk management of these products, which requires precise, up-to-date, and frequent valuation, poses a significant computational challenge for insurance companies, especially in large portfolios. Today, common and traditional methods such as Monte Carlo simulation, are often inefficient for risk management of these products due to their time-consuming nature. In recent years, with the emergence of modern technologies such as meta-modeling methods as a solution to these challenges, they have been proposed, but unfortunately, limited research has focused on increasing the adaptability and intelligence of these secondary models when facing changing data. In this regard, to address this challenge, the main objective of this study is to improve the adaptability and intelligence of meta-models by developing an intelligent meta-model based on artificial intelligence that can value large variable annuity portfolios more quickly and accurately. As a result, this would enable better responsiveness to ongoing market developments and portfolio structure changes.METHODS: In this study, to address data changes, we need to improve the adaptability and intelligence of meta-models, which will consequently improve their responsiveness to ongoing developments in the market and portfolio structure. This improvement is achieved through the development of methods for intelligently determining the number of representative contracts, optimally selecting a subset of contracts, and choosing the most accurate underlying valuation models. For this purpose, a suite of models and methods will be evaluated to assess their effectiveness and suitability in this context. Therefore, the methodology of this research involves the design and implementation of an intelligent meta-model, whose performance is evaluated using five well-known machine learning models from among machine learning models and according to the evaluation criteria commonly used in these methods. The main emphasis of the proposed approach in this study is on improving model accuracy through innovative approaches in data preprocessing and effective sampling, enabling the models so that the chosen models can generalize complex patterns to new data rather than merely memorizing them. These evaluations are conducted using standard metrics, and ultimately, the integration of these elements into an intelligent system aims to provide a final validation of the potential of artificial intelligence in this field.FINDINGS: The results of this study confirm the effectiveness of artificial intelligence in enhancing the meta-models for valuing variable annuities. The final meta-model obtained from the proposed approach demonstrated highly accurate performance according to standard metrics, indicating the model’s ability to cope with the valuation complexities of large insurance portfolios. The key factors of this success include the high importance of data preprocessing, targeted sampling, and the selection of efficient machine learning models as representative models. The results indicate that although increasing the size of the dataset for the meta-model may not necessarily improve prediction accuracy and efficiency, the use of optimization algorithms for selecting and tuning the model’s parameters can significantly improve performance. Therefore, the findings emphasize the important role of parameter optimization over merely increasing the training data size in enhancing the final model’s accuracy and efficiency in prediction.CONCLUSION: This research focuses on intelligent enhancement of meta-models through artificial intelligence to transform variable annuity portfolio management. The main finding of this study is a significant improvement in the quality and accuracy of the obtained results in valuation and risk management of the large portfolios of variable annuities in insurance industry. For this, by integrating precise and reliable methodologies into the proposed meta-model structure, enabling insurance companies to manage their portfolios of variable annuities with greater confidence and provide better service to policyholders. These results not only enhance the credibility and significant rolls of novel approach of artificial intelligence applications in the insurance industry but also offer practical insights for companies to develop stronger and more adaptable valuation and risk management models in the face of the financial complexities inherent in variable annuities.

Original Research Paper Marketing and Sales

Identifying and Explaining Marketing Strategies of Liability Insurance: A Thematic Analysis Approach

Elahe Mollaei; Sanaz Shafiee

Articles in Press, Accepted Manuscript, Available Online from 14 July 2026

Abstract

BACKGROUND AND OBJECTIVES: Employee liability insurance, which protects employees against workplace accidents and provides employers with financial security, is a crucial yet underdeveloped area of the insurance sector in Iran. In many advanced economies, this type of insurance has become a core component ... Read More BACKGROUND AND OBJECTIVES: Employee liability insurance, which protects employees against workplace accidents and provides employers with financial security, is a crucial yet underdeveloped area of the insurance sector in Iran. In many advanced economies, this type of insurance has become a core component of organizational risk management, employee welfare, and corporate responsibility, contributing significantly to productivity, retention, and sustainable growth. However, in Iran, the penetration rate of liability insurance remains far below the global average, leaving a large segment of the workforce exposed to workplace risks without adequate financial protection. This gap highlights an urgent need for a multidimensional marketing framework that integrates ethical, organizational, communicative, and supportive strategies. The present study was designed to fill this gap by exploring the perceptions of both insurance experts and employers—two key stakeholder groups directly engaged with employee liability insurance. The research sought to answer two central questions:1. What are the key factors influencing the marketing strategies of employee liability insurance in Iran?2. What practical approaches can enhance the adoption and development of such strategies in the Iranian context?METHODS: This study adopted an exploratory qualitative design with a focus on thematic analysis. The applied nature of the research aligns with its objective of generating practical solutions for the insurance sector. Participants included fourteen senior managers and experts from six prominent Iranian insurance companies (Parsian, Dey, Tamin, Kosar, Dana, and Asia), selected through purposive sampling based on the principle of theoretical saturation. Eligibility criteria included a minimum of ten years of experience in the insurance industry, familiarity with marketing strategy concepts, willingness to participate, and holding at least a master’s degree.Data were collected through semi-structured interviews, each lasting 45–60 minutes. All interviews were recorded with participant consent, transcribed verbatim, and coded using Attride-Stirling’s six-step thematic network analysis. This systematic approach involved: repeated reading of transcripts, extraction of initial codes, grouping into basic themes, aggregation into organizing themes, identification of global themes, and interpretive synthesis. Through this process, 28 distinct factors were identified and consolidated into four overarching categories: ethical factors, organizational factors, communication-based strategies, and supportive strategies.FINDINGS: The thematic analysis revealed that the marketing of employee liability insurance in Iran is shaped by a multidimensional set of drivers and barriers, clustered into four core domains:Ethical considerations emerged as the most fundamental pillar for building trust in the insurance sector. Participants emphasized that transparency in communications, clarity in financial processes, and adherence to ethical norms are not optional values but essential prerequisites for effective marketing. The lack of transparency in claims settlement, frequently cited by participants, has fueled mistrust among both employers and employees. Responsibility toward stakeholders and demonstration of corporate social responsibility were seen as essential to repositioning insurance companies as partners in employee welfare rather than mere profit-seekers.Internal organizational practices—such as cultivating a supportive corporate culture, continuous training, participatory decision-making, innovation, and effective crisis management—were identified as critical enablers of successful marketing strategies. Experts stressed that employee liability insurance must be integrated into broader human resource policies to shift employer perceptions from viewing it as a financial burden to recognizing it as a strategic investment in workforce stability and productivity.The third dimension underscored the role of consistent, two-way communication with stakeholders. Participants highlighted the need for establishing feedback loops, loyalty programs, and collaborative cross-sectoral initiatives. Traditional one-way advertising was considered insufficient in today’s environment, where relationship-building and long-term trust are key. By designing interactive communication channels and fostering sustained engagement, insurers can improve both uptake and retention rates of liability insurance policies.A relatively novel contribution of this study was the identification of supportive strategies, including promoting mental health initiatives, ensuring diversity and inclusion, and implementing incentive systems. These approaches extend the perception of insurance beyond financial protection, framing it instead as part of a holistic welfare package. Such strategies not only improve the attractiveness of liability insurance for employers but also enhance employee satisfaction and loyalty.CONCLUSION: This study demonstrates that the successful marketing of employee liability insurance in Iran requires a multidimensional and innovative approach. Ethical transparency and social responsibility must form the foundation upon which insurers build trust with employers and employees. Organizational changes—such as embedding insurance within human resource policies and linking it to employee development—can transform liability insurance from a compliance-driven cost into a strategic asset. Communication strategies must go beyond traditional advertising to foster interactive, long-term relationships, while supportive initiatives addressing employee well-being and diversity can differentiate insurers in a competitive market. From a theoretical standpoint, the study contributes a comprehensive framework that integrates ethical, organizational, communicative, and supportive dimensions into insurance marketing strategies.

Original Research Paper New Insurance Technologies

Identifying and Ranking key Strategies for Implementing Insurtech in Insurance Startups

Amir Bahrami; Shabnam Refoua

Articles in Press, Accepted Manuscript, Available Online from 21 July 2026

Abstract

BACKGROUND AND OBJECTIVES: The insurance industry, as one of the vital and important pillars of every country's economy, has always faced various challenges, including increased competition, the complexity of risk management, and changes in customer behavior and needs over time. The introduction of new ... Read More BACKGROUND AND OBJECTIVES: The insurance industry, as one of the vital and important pillars of every country's economy, has always faced various challenges, including increased competition, the complexity of risk management, and changes in customer behavior and needs over time. The introduction of new information and communication technologies, as well as the use of up-to-date technologies, has provided new opportunities for the transformation of the insurance industry. With the rapid advancement of digital technologies and increasing competition in the insurance industry, insurtechs and insurance startups are emerging as key players in providing innovative services and playing a vital role in improving customer experience and providing more efficient services to policyholders. However, the success of insurance startups and their operationalization depends on the proper design and implementation of appropriate strategies in various areas of new technologies. The present study aims to identify and prioritize key strategies for implementing InsurTech in insurance startups in the dimensions of data and information exchange, market and customer, competition and differentiation, technology and infrastructure, business model, operations and organization, and risk and compliance.METHODS: The research method of the present study is of an applied type in terms of purpose and of a descriptive-analytical type in terms of the nature of the research. The research methodology is a mixed approach (qualitative-quantitative) And in the qualitative stage, effective strategies for implementing InsurTech in insurance startups were identified through an extensive review of specialized theoretical literature in the fields of insurance and new technologies related to the insurance industry, as well as through interviews with experts, including insurance startup managers, senior insurance technology experts, and insurance company managers and specialists. In the quantitative phase, using the Analytic Hierarchy Process (AHP) technique and multi-criteria decision-making tools, the strategies identified from the qualitative phase were prioritized based on importance, feasibility, and impact on the success of startups. FINDINGS: The results of the research show that, in line with the research objectives and using qualitative analysis, seven key strategies were identified based on the experts' opinions: data and information exchange, technology and infrastructure, market and customer competition and differentiation, business model, operations and organization, and risk and compliance. On the other hand, according to one of the other objectives of the present study, prioritization of identified key strategies has been carried out based on the analytic hierarchy process technique. Based on the ranking, data and information exchange strategies, technology, and infrastructure play the greatest role in implementing InsurTech in insurance startups. In addition, competitive and differentiation, market and customer strategies, as well as increasing customer loyalty and increasing the speed of response to market changes, play a significant role. On the other hand, business model, operations and organization strategies, as well as risk and compliance, have a less important role and importance in the implementation of InsurTech in insurance startups.CONCLUSION: Digital transformation has fundamentally changed the insurance industry in recent years. Insurtech, as a combination of new technologies and insurance services, plays a significant role in developing innovative products, improving customer experience, reducing costs, and increasing operational efficiency. Insurance startups, as pioneers in this field, need to adopt coherent and coordinated strategic approaches to succeed. Identifying and prioritizing key strategies helps insurance startups allocate their resources optimally and effectively, make more effective strategic decisions, and pave the way for sustainable growth and development and creating competitive advantage. A simultaneous focus on technology and infrastructure, business model innovation, data and customer management, and risk adaptation provides the foundation for long-term success and differentiation in the competitive insurance market. The research results show that Insurtech implementation strategies lead to effective results when they are implemented in an integrated, phased manner, and in proportion to the strength and capacity of insurance startups.CONCLUSION: Digital transformation has fundamentally changed the insurance industry in recent years. Insurtech, as a combination of new technologies and insurance services, plays a significant role in developing innovative products, improving customer experience, reducing costs, and increasing operational efficiency. Insurance startups, as pioneers in this field, need to adopt coherent and coordinated strategic approaches to succeed. Identifying and prioritizing key strategies helps insurance startups allocate their resources optimally and effectively, make more effective strategic decisions, and pave the way for sustainable growth and development and creating competitive advantage. A simultaneous focus on technology and infrastructure, business model innovation, data and customer management, and risk adaptation provides the foundation for long-term success and differentiation in the competitive insurance market. The research results show that Insurtech implementation strategies lead to effective results when they are implemented in an integrated, phased manner, and in proportion to the strength and capacity of insurance startups.

Original Research Paper Insurance Social Studies

The role of social security support insurance on women’s participation in the carpet‑weaving industry; a case study of Takab County

Afsaneh Tavassoli; Gol Sanam Gholami gole gole

Articles in Press, Accepted Manuscript, Available Online from 21 July 2026

Abstract

Background and Objectives: Iranian handmade carpets are a cultural and economic commodity and one of the most valuable non-oil products that has provided employment for activists in this field throughout the ages. Weavers are the main link in the handmade carpet production cycle. More than seventy percent ... Read More Background and Objectives: Iranian handmade carpets are a cultural and economic commodity and one of the most valuable non-oil products that has provided employment for activists in this field throughout the ages. Weavers are the main link in the handmade carpet production cycle. More than seventy percent of Iranian carpet weavers are women in urban and rural areas. The carpet weaving industry of the city of Takab in West Azerbaijan Province, with the global registration of "Iron Carpets" in WIPO and an annual production history of about 15,000 square meters of handmade carpets, is one of the rare examples of Iranian carpets that has faced a decrease in the participation of women carpet weavers in production in recent years. For sustainable employment in carpet production, it is essential to create protective insurance coverage and continuity of insurance. Continuous implementation of macro policies in the management of protective insurance for employees in this sector is essential.Methodology: This is a mixed study (quantitative and qualitative). The statistical population of the research is 300 women members of the Takab Carpet Weaving Union who had insurance and a registered history in that unit in 1403. Based on the Cochran formula, the statistical sample of the study was 168 people. The systematic sampling method was systematic. The research tool was a standardized and previously tested researcher-made questionnaire, which showed a high coefficient of validity of the questionnaires for the continuation of the study. Findings: After analyzing the questionnaires with the help of SPSS and hypothesis testing, which was carried out with two descriptive and inferential statistical methods, we found that there is a negative and inverse relationship between weak management and policy-making. There is also a negative and inverse relationship between the improper functioning of insurance and financial services and women's participation in the carpet weaving industry. There is a positive and direct relationship between the subcomponents of age, income, carpet weaving history and women's participation in the carpet weaving industry. There was a significant difference between the subcomponent of job status and women's participation in the carpet weaving industry.Conclusion: The findings of the study led us to the conclusion that the factors affecting the decline in the participation of women in Takab carpet weaving are more at the macro-level of management and policy-making than at the individual and regional level. It seems that the lack of continuous insurance for women weavers is one of the reasons for the withdrawal of weavers from the production cycle of this industry. Comprehensive and sustainable insurance services as part of the supportive approach of policymakers can be helpful. The results showed that with legislation, management assistance in providing appropriate insurance and financial services, women's participation in the handmade carpet production cycle can be increased. The conditions for the weavers' job survival have improved and in the long run, it is likely to benefit the regional carpet weaving industry and women carpet weavers. The hand-woven carpet industry, as an indigenous Iranian industry that simultaneously represents the authenticity of Iranian culture, art, and economy, today faces obstacles and challenges for growth and development. A vast majority of the workforce in this industry are women. This study exclusively focuses on female carpet weavers as the central link in the production chain of this product. In the theory of social comparison processes, individuals evaluate their own position in relation to the positions of others. This comparison is assessed in terms of inputs (what the individual contributes to the environment) and outputs (what they receive in terms of rights, benefits, social relationships, and reputation). The findings of this study, in comparison with previous research and the obtained variables, indicate that the inputs of women as carpet weavers include producing valuable products with global credibility and quality. In this context, marital status and income also serve as driving forces for women's carpet weaving activities. However, the outputs for this group—such as comprehensive policies for continuous support through adequate insurance and appropriate social rights—are not proportionate to each other. Today, a gender-sensitive approach plays a significant role in improving quality and enhancing development plans. It seems that most of the previously enacted economic support laws were gender-neutral and were designed and implemented solely with an economic and financial approach to promote and sustain production themes. To improve and elevate the current conditions, it is better to address the existing gaps in this field by conducting qualitative field research and focusing on studies centered on gender and carpet weaving. Efforts should be made to raise awareness among female carpet weavers about support laws and regulations, streamline bureaucratic processes, and develop and diversify specialized insurance for carpet weavers to change the attitudes of female weavers toward support programs.

Original Research Paper Loss adjuster in insurance

Analysis of the Effect of Exchange Rate Fluctuations on the Profitability of Insurance Companies that are Members of the Stock Exchange in Iran

MAHMOUD Shirjazi; mostafa rajabi

Articles in Press, Accepted Manuscript, Available Online from 22 July 2026

Abstract

BACKGROUND AND OBJECTIVES: A characteristic of developing countries is resource constraints, particularly foreign exchange resources. Given their production structure and foreign exchange constraints, these countries require appropriate exchange rate policies to mitigate exchange rate volatility and ... Read More BACKGROUND AND OBJECTIVES: A characteristic of developing countries is resource constraints, particularly foreign exchange resources. Given their production structure and foreign exchange constraints, these countries require appropriate exchange rate policies to mitigate exchange rate volatility and its adverse economic effects. In Iran, economic conditions and sanctions have imposed severe exchange rate fluctuations on the economy in the past decade. Meanwhile, insurance companies, due to the nature of their services and their status as listed entities, are affected by exchange rate fluctuations in multiple dimensions. The insurance sector plays a fundamental role in economic growth, resource allocation, liquidity creation, economies of scale in investments, and loss distribution. The insurance industry is a key indicator of development, especially financial market development, and a major economic institution that supports the activities of other entities. With its extensive human resources and a broad network of branches, agencies, and other insurance units, it holds a significant role in countries' economic development. Indeed, the insurance industry is a subsector of the financial market with broad spillover effects on other markets and should guarantee the health of financial markets and economic activities (Crabbe, 2012). Therefore, this research aims to analyze the effect of exchange rate fluctuations on the profitability of 14 insurance companies listed on the Tehran Stock Exchange during the period 2011-2023.Existing limited research presents conflicting results regarding the effect of exchange rate volatility on corporate profitability, with some researchers finding a significant positive effect and others a significant negative effect. This indicates a lack of consistent evidence, particularly within the insurance industry, on the relationship between exchange rate volatility and profitability. Amoabeng and Yohane (2025), in a study on "The Impact of Exchange Rate Fluctuations on the Financial Performance of Insurance Companies in Zambia," found that while most respondents reported profitability growth over the past five years, exchange rate volatility was perceived as a factor with a moderate impact on financial performance, though 34% considered its impact very significant. Affected areas included profit margins, operational costs, and claim payments. Ramal (2023) examined "The Performance Index of Pakistan's Insurance Industry in Exchange Rate Fluctuations," finding that currency depreciation and other factors arising from exchange rate fluctuations have a primary impact on profitability, and that exchange rate volatility and profitability are inversely related in the top Pakistani insurance companies studied.Given that Iran's insurance industry also experiences these diverse effects created by foreign exchange fluctuations—while reports show an increasing trend in insurance company profits over the past decade, they do not clearly indicate whether exchange rate volatility has contributed to these profits—this study seeks to provide a precise answer to the question: What is the impact of exchange rate fluctuations on the profitability of insurance companies, particularly those listed on the Tehran Stock Exchange? Furthermore, as insurance companies in general, and listed ones in particular, are affected by these fluctuations in terms of both income and expenses due to the broad scope of their services and their investments in the stock market, analyzing the effect of these fluctuations on their performance, manifested as profitability, is crucial. It is essential for various insurance companies to hedge against these fluctuations to enhance their profitability and prevent a decline in their stock prices and corporate value. Hence, the objective of this research is to analyze the impact of exchange rate volatility on the profitability of insurance companies listed on the Tehran Stock Exchange in Iran..METHODS: The purpose of this research was to analyze the effect of exchange rate fluctuations on the profitability of 14 insurance companies that are members of the stock exchange in the period of 2011-2014. In this sense and according to the structure of the data, the panel data method was used and after conducting related tests, the research model was estimated using the random effects method In terms of research method, this study is correlational in nature and content. It utilizes secondary data extracted from the financial statements of insurance companies listed on the Tehran Stock Exchange to conduct correlation analysis. Its reasoning method is inductive-deductive to discover relationships between variables through correlation. Since correlational research is a subset of descriptive research, this study falls into the descriptive research category. Also, due to its use of past data and statistics related to variables, it is ex-post facto. Structurally, the data is panel data, and considering various methods for such data, the analysis is causal and panel-based. Therefore, this research can be considered applied, utilizing economic theories and regularities;. Data analysis was performed using Eviews software.The statistical population of the study includes insurance companies listed on the Tehran Stock Exchange. A systematic elimination (screening) sampling method was used to select the statistical sample, meaning from among all companies listed on the Tehran Stock Exchange.FINDINGS: The findings showed that exchange rate fluctuations have a negative and significant effect on the profitability of insurance companies according to their type of activity. Also, macro-economic variables such as inflation and growth of share have a positive and significant effect and internal variables of the company such as the ratio of capital to assets and the ratio of debt to assets and the size of the company have also had a positive and significant effect on the profitability of companiesCONCLUSION: The result shows that the adoption of appropriate currency policies to reduce currency fluctuations can have an increasing effect on the performance of insurance companies

Original Research Paper Future research in the insurance industry

A Model for Developing New Technologies in the Iranian Insurance Industry Using a Futures Studies Approach

Mohammad Mahmoudi Maymand; Saeideh Seyadat; IMAN Azizi

Articles in Press, Accepted Manuscript, Available Online from 08 August 2026

Abstract

چکیده مبسوط زمینه و اهمیت پژوهش: تحول دیجیتال در دهه اخیر به یکی از مؤلفه‌های کلیدی رقابت‌پذیری در صنعت بیمه تبدیل شده است؛ بااین‌حال، شواهد نشان می‌دهد که استقرار ... Read More چکیده مبسوط زمینه و اهمیت پژوهش: تحول دیجیتال در دهه اخیر به یکی از مؤلفه‌های کلیدی رقابت‌پذیری در صنعت بیمه تبدیل شده است؛ بااین‌حال، شواهد نشان می‌دهد که استقرار صرف فناوری‌های نوین لزوماً به هوشمندی راهبردی و آینده‌پژوهی مؤثر منجر نمی‌شود. تحقق این هدف مستلزم وجود سازوکارهای نهادی برای کنترل ریسک، امنیت اطلاعات و حکمرانی داده است. صنعت بیمه ایران در مواجهه با فناوری‌هایی مانند هوش مصنوعی، کلان‌داده و اکوسیستم‌های دیجیتال، همچنان فاقد چارچوبی بومی و منعطف برای هدایت این تحول است. هدف پژوهش: پژوهش حاضر با هدف طراحی و آزمون مدلی مفهومی و تجربی برای تبیین سازوکارهای مؤثر بر توسعه فناوری‌های نوین در صنعت بیمه با تمرکز بر نقش میانجی ریسک، امنیت و حکمرانی داده است. روش‌شناسی پژوهش: این پژوهش با رویکرد آمیخته (کیفی–کمی) انجام شده است. در بخش کیفی، به‌منظور شناسایی و اجماع بر عوامل مؤثر بر توسعه فناوری‌های نوین در صنعت بیمه، از روش دلفی چندمرحله‌ای استفاده شد. جامعه این بخش شامل خبرگان صنعت بیمه، فناوری و تنظیم‌گری بود که به‌صورت هدفمند و قضاوتی انتخاب شدند. پانل نهایی دلفی متشکل از 17 نفر خبره با بیش از 10 سال سابقه حرفه‌ای بود. داده‌های راند نخست از طریق پرسش‌های باز گردآوری و با تحلیل محتوای نظام‌مند بررسی شد. در راندهای بعدی، سطح اجماع با شاخص‌هایی مانند میانگین، میانه، دامنه بین‌چارکی و ضریب تغییرات سنجیده و روایی محتوای عوامل با CVR و CVI تأیید گردید. خروجی این مرحله، استخراج عوامل نهایی و طراحی مدل مفهومی پژوهش بود.در بخش کمی، داده‌ها با استفاده از پرسشنامه محقق‌ساخته مبتنی بر نتایج دلفی و با مقیاس لیکرت پنج‌درجه‌ای گردآوری شد. جامعه آماری شامل مدیران و کارشناسان شرکت‌های بیمه، نهادهای تنظیم‌گر و شرکت‌های اینشورتک بود. حجم نمونه بر اساس فرمول کوکران برابر با 242 نفر محاسبه شد. تحلیل داده‌ها با روش مدل‌سازی معادلات ساختاری مبتنی بر حداقل مربعات جزئی (PLS-SEM) و نرم‌افزار SmartPLS انجام شد. یافته‌های پژوهش: یافته‌های بخش کیفی پژوهش که با استفاده از روش دلفی طی سه راند و با مشارکت 17 نفر از خبرگان صنعت بیمه، فناوری و سیاست‌گذاری بیمه‌ای انجام شد، منجر به استخراج و اجماع بر چهار بعد پیشران اصلی شامل فناوری‌های نوین و زیرساخت دیجیتال، سازمانی–مدیریتی، رگولاتوری–نهادی و مشتری–بازار گردید. همچنین، خبرگان به‌صورت معناداری بر نقش محوری سازه‌ی «ریسک، امنیت و حکمرانی داده» به‌عنوان سازوکار واسط و تنظیم‌کننده در فرآیند توسعه فناوری‌های نوین تأکید داشتند. نتایج شاخص‌های روایی محتوا (CVR و CVI) نشان داد که تمامی سازه‌ها و گویه‌های نهایی از روایی محتوایی مطلوب برخوردار بوده و مدل مفهومی اولیه از منظر خبرگان دارای انسجام نظری و کفایت تبیینی است.نتایج برآورد مدل ساختاری نشان می‌دهد که سازه‌ی ریسک، امنیت و حکمرانی داده نقشی محوری و واسط در تبیین روابط علّی میان ابعاد پیشران و پیامد نهایی پژوهش ایفا می‌کند. تحلیل مسیرها بیانگر آن است که کیفیت حکمرانی داده، سازوکار اصلی تبدیل ورودی‌های فناورانه، سازمانی و نهادی به آینده‌پژوهی و هوشمندی راهبردی سازمانی است. در این میان، بعد مشتری و بازار با ضریب مسیر 0.530 بیشترین اثر مثبت و معنادار را بر سازه‌ی ریسک، امنیت و حکمرانی داده دارد که نشان‌دهنده نقش تعیین‌کننده انتظارات مشتریان، الزامات اعتماد دیجیتال و فشارهای رقابتی در شکل‌گیری این سازوکارهاست.همچنین، ابعاد فناوری‌های نوین و زیرساخت دیجیتال، سازمانی و مدیریتی و رگولاتوری و نهادی به‌ترتیب اثرات مثبت و معناداری بر سازه‌ی ریسک، امنیت و حکمرانی داده نشان می‌دهند. این یافته‌ها تأیید می‌کند که بلوغ فناوری و یکپارچگی زیرساخت‌ها، شایستگی‌های مدیریتی و فرهنگ داده‌محور، و نیز قوانین و الزامات نظارتی، پیش‌شرط‌های نهادی استقرار مؤثر حکمرانی داده و مدیریت ریسک دیجیتال در صنعت بیمه هستند.در گام بعد، سازه‌ی ریسک، امنیت و حکمرانی داده اثر مستقیم، قوی و معناداری بر آینده‌پژوهی و هوشمندی راهبردی دارد (β = 0/727)، که به‌عنوان قوی‌ترین مسیر مدل شناسایی شد. این نتیجه نشان می‌دهد ارتقای کیفیت حکمرانی داده، توان سازمان‌های بیمه را در تحلیل روندهای محیطی، پیش‌بینی سناریوهای آینده و تصمیم‌گیری راهبردی مبتنی بر داده به‌طور معناداری افزایش می‌دهد. از نظر قدرت تبیین، مقادیر 2R^ برای سازه‌ی ریسک، امنیت و حکمرانی داده (49/0) و آینده‌پژوهی و هوشمندی راهبردی (53/0) بیانگر توان قابل‌قبول مدل در تبیین متغیرهای درون‌زا و تأیید نقش حکمرانی داده به‌عنوان مجرای اصلی انتقال اثرات پیشران‌ها به سطح راهبردی صنعت بیمه است.بحث و نتیجه‌گیری: تحلیل نهایی پژوهش نشان می‌دهد که تحول دیجیتال در صنعت بیمه ایران را نمی‌توان با الگوهای خطی و صرفاً مبتنی بر سرمایه‌گذاری فناورانه تبیین کرد، بلکه این تحول مستلزم استقرار مدل‌های تطبیقی و داده‌محور است. در این میان، ریسک، امنیت و حکمرانی داده به‌عنوان فیلتر علّی و تنظیم‌کننده‌ی تعامل میان ابعاد فناورانه، سازمانی، رگولاتوری و بازار ایفای نقش می‌کند. نتایج کمی مدل ساختاری نشان داد که ریسک، امنیت و حکمرانی داده دارای اثر مستقیم، قوی و معنادار بر آینده‌پژوهی و هوشمندی راهبردی سازمانی است و بدون آن، آثار سرمایه‌گذاری دیجیتال پراکنده و ناپایدار باقی می‌ماند. در چارچوب آینده‌پژوهی سازمانی، حکمرانی داده امکان گذار شرکت‌های بیمه از بهره‌گیری ابزاری از فناوری به شکل‌گیری ساختارهای یادگیرنده و تصمیم‌سازی مبتنی بر داده را فراهم می‌کند. از منظر نظری، پژوهش با برجسته‌سازی نقش میانجی ریسک، امنیت و حکمرانی داده ، الگویی تلفیقی از تحول بیمه‌ای ارائه می‌دهد که قابلیت تعمیم به سایر صنایع داده‌محور را نیز دارد. در سطح عملی و سیاستی، نتایج بر ضرورت اولویت‌دهی به استقرار چارچوب‌های یکپارچه حکمرانی داده و مدیریت ریسک دیجیتال به‌عنوان بستر تحول راهبردی تأکید دارد. در نهایت، مدل پیشنهادی حکمرانی داده را به‌مثابه سازه‌ای راهبردی برای حرکت صنعت بیمه به‌سوی آینده‌ای مبتنی بر هوشمندی، اعتماد دیجیتال و ریسک‌پذیری کنترل‌شده معرفی می‌کند.

Original Research Paper Insurance rights

A Comparative Study on the Limits of Third-Party Motor Vehicle Insurer’s Liability in the Legal Systems of Iran, the United States, and France

Zahra Asadi; Leili Niakab

Articles in Press, Accepted Manuscript, Available Online from 08 August 2026

Abstract

BACKGROUND AND OBJECTIVES: Third-party liability insurance in the domain of traffic accidents represents one of the main protective mechanisms in modern societies. The increasing number of vehicles and the financial and social consequences arising from road accidents highlight the necessity of a reliable ... Read More BACKGROUND AND OBJECTIVES: Third-party liability insurance in the domain of traffic accidents represents one of the main protective mechanisms in modern societies. The increasing number of vehicles and the financial and social consequences arising from road accidents highlight the necessity of a reliable framework for compensating victims. Compulsory third-party insurance, as a primary and mandatory instrument, plays a vital role in this regard.Lawmakers in different legal systems have established various frameworks and limits concerning the liability of third-party insurers, usually defining the minimum scope and responsibilities of the insurer.The main objective of this research is to examine and compare the extent of third-party insurer liability in the legal systems of Iran, France, and the United States, focusing on the scope of financial and bodily commitments. Based on this comparative analysis, the study ultimately provides recommendations for improving the Iranian legal framework.METHODS: This study adopts a descriptive-analytical approach combined with a comparative legal method, examining and contrasting the selected legal systems. The core basis of analysis includes statutes, regulations, and legal doctrines related to third-party liability insurance in the legal systems of Iran, France, and the United States (with an emphasis on prominent state models).FINDINGS: The comparative analysis of the legal systems of Iran, France, and the United States reveals significant differences in determining the scope of third-party insurers’ financial and bodily obligations:Iran:The Compulsory Insurance Act (2016) has extended the insurer’s commitments up to a specified limit. However, in the case of bodily damages, legal caps (such as the fixed Diyah—blood money) and restrictions on covering certain medical and non-material costs result in a persistent gap between the actual damage suffered by victims and the indemnity paid. In the area of property damage, fixed coverage limits are often insufficient in the face of widespread losses and inflation.United States:Diversity across states is a defining feature. In no-fault liability states, the victim’s own insurer compensates bodily injuries up to a predetermined limit without requiring proof of fault—an arrangement that expedites compensation and reduces litigation delays but may increase overall premium costs. Conversely, in fault-based states, the insurer is obligated to pay only after the at-fault party’s liability is proven, which aligns more closely with the principle of individual justice but complicates and prolongs the compensation process.France:The French legal system emphasizes the principle of “full compensation for damages.” The third-party insurer is typically obliged to cover all bodily injuries suffered by the victim, including medical expenses, disability, moral damages, and financial losses due to income reduction. Additionally, the financial coverage limits are relatively high and are periodically adjusted in line with European inflation indices. This approach ensures the highest level of protection for victims.Overall, the French legal system provides the widest scope of financial and bodily liability for third-party insurers. The Iranian system, despite notable progress, still faces restrictions in both areas. The American system, with its state-specific diversity, adopts variable coverage approaches depending on regional requirements.CONCLUSION: A comparative analysis of the third-party liability insurance limits in the legal systems of Iran, France, and the United States reveals that determining these limits is the result of a complex balancing act between two fundamental goals: achieving compensatory justice for victims and ensuring the economic stability of the insurance industry. Each system, considering its cultural, economic, and legal context, has adopted a different approach. The French legal system, by prioritizing the full compensation of damages, aligns most closely with the idea of justice; however, this approach imposes a significant financial burden on the insurance industry, necessitating special support mechanisms. The American legal system, acknowledging the fundamental differences among states and adapting its approaches to the specific regional needs of each, has strived to establish an efficient balance between the requirements of economic efficiency and the necessity of protecting victims. This federal approach provides high flexibility but may lead to disparities in the level of protection. The Iranian system, while extending the scope of the insurer’s obligations to a certain ceiling, still faces challenges in fully covering damages, making legal reforms and increasing public awareness essential for addressing them. Ultimately, finding an ideal solution that simultaneously serves the interests of all stakeholders remains a fundamental challenge in the law of civil liability insurance.

Original Research Paper financial markets

Capital Market Reaction to Disclosure Delay and Report Textual Quality: Evidence from the Iranian Insurance Industry

Mohammad Ebrahim Raei Ezabadi; Babak Nabati

Articles in Press, Accepted Manuscript, Available Online from 08 August 2026

Abstract

BACKGROUND AND OBJECTIVES: The insurance industry in Iran, with 42 active firms, accounts for a relatively limited share of the total capitalization of the Iranian capital market. Nevertheless, its economic and informational importance is greater than what its market share alone may suggest. Insurance ... Read More BACKGROUND AND OBJECTIVES: The insurance industry in Iran, with 42 active firms, accounts for a relatively limited share of the total capitalization of the Iranian capital market. Nevertheless, its economic and informational importance is greater than what its market share alone may suggest. Insurance companies operate in a highly risk-oriented environment, where underwriting decisions, claim obligations, technical reserves, investment activities, and solvency conditions are closely linked to uncertainty and future expectations. For this reason, financial and managerial disclosures issued by insurance firms can provide market participants with important signals about risk exposure, operational performance, managerial outlook, and the credibility of corporate reporting. In such a context, the timing of information disclosure becomes particularly important, because delays in releasing information may affect investors’ perceptions of transparency, information asymmetry, and reporting reliability. At the same time, the textual quality of disclosure reports may theoretically influence how investors interpret and process disclosed information, especially when the reports contain risk-related or forward-looking content. However, whether these textual characteristics are actually reflected in short-term capital market reactions remains an empirical question. Accordingly, this study aims to simultaneously analyze the timeliness of information disclosure and the textual quality of disclosure reports, and to examine their roles in shaping capital market reactions within Iran’s insurance industry.METHODS: This study is applied in terms of purpose and quantitative in nature, and it was conducted within the positivist research paradigm. The empirical analysis is based on data from 19 insurance companies listed on the Tehran Stock Exchange over the period 2015–2024. In the first stage, the market reaction to information disclosure was measured using the event study methodology. For this purpose, logarithmic cumulative abnormal returns (CAR) were calculated for the shares of the sample firms across several event windows surrounding the disclosure dates. This approach made it possible to capture short-term abnormal stock return behavior before and after the release of disclosed information. In the next stage, textual disclosure quality indicators were extracted using natural language processing techniques. Specifically, contextual language representations generated by the ParsBERT model were used to measure semantic similarity in relation to two disclosure content dimensions: risk disclosure and forward-looking disclosure. These semantic similarity measures were employed as proxies for the textual quality of disclosure reports. Subsequently, the effects of both the occurrence of disclosure delay and the magnitude of disclosure delay, together with textual disclosure quality, on capital market reactions were analyzed. The empirical tests were conducted using linear regression models based on ordinary least squares (OLS), a logit model for examining the direction of market reaction, and gradient boosting–based machine learning algorithms as complementary tools for assessing nonlinear relationships and the robustness of the results.FINDINGS: The event study results indicate that the capital market exhibits an immediate and predominantly negative reaction to delayed information disclosure, with this reaction being discontinuously concentrated on the disclosure day. Regression results show that the occurrence of disclosure delay has a statistically significant negative effect on logarithmic cumulative abnormal returns, whereas delay severity and textual disclosure quality measures do not exhibit a stable or significant impact on the magnitude of market reactions. The logit model results show that the occurrence of disclosure delay is associated with an increased likelihood of a positive market reaction. By contrast, delay severity reduces this likelihood. Disclosure quality has no significant effect on the direction of market reaction in the short-term window; however, in the medium-term window (-2, +2), its interaction with delay severity is positive and statistically significant, suggesting that the relationship between delay severity and the likelihood of a positive market reaction may vary depending on the level of disclosure quality. Furthermore, the weak performance of machine learning models in predicting the continuous value of logarithmic cumulative abnormal returns indicates the difficulty of predicting the intensity of market reactions over short-term horizons.CONCLUSION: Market reaction to information disclosure is multidimensional. In the linear models, the occurrence of delay is associated with a reduction in cumulative abnormal returns, whereas in the logit model, the occurrence of delay is associated with an increased likelihood of a positive market reaction. Textual disclosure quality has no significant effect on the magnitude of CAR in the linear models; however, in the logit and machine learning models, it may contribute to explaining the direction of market reaction. This distinction is important because different empirical models capture different aspects of investors’ responses to disclosed information. Therefore, the effect of disclosure delay should be interpreted by distinguishing between the magnitude of returns and the direction of market reaction.

Original Research Paper Risk assessment in insurance

Predicting the Direct and Indirect Effects of War on the Insurance Industry with Emphasis on Fire Insurance: A Comparative Study of Syria, Ukraine, and Lebanon

Saeed Asadi gharagoz

Articles in Press, Accepted Manuscript, Available Online from 17 August 2026

Abstract

BACKGROUND AND OBJECTIVES: War, as a catastrophic systemic risk, destroys physical and human infrastructure and inflicts deep, persistent shocks on macroeconomic variables and financial markets, including the insurance industry. The "The Iran-Israel-US war (June 2025 – April 2026)"caused widespread ... Read More BACKGROUND AND OBJECTIVES: War, as a catastrophic systemic risk, destroys physical and human infrastructure and inflicts deep, persistent shocks on macroeconomic variables and financial markets, including the insurance industry. The "The Iran-Israel-US war (June 2025 – April 2026)"caused widespread destruction, with over 115,000 civilian units damaged and preliminary damage estimates of approximately USD 270 billion. This study aims to quantify the direct and indirect impacts of war on the loss ratio and insurance penetration in Iran by leveraging the experiences of three war-affected countries: Syria (civil war since 2011), Ukraine (Russian invasion since 2022), and Lebanon (Israel-Hezbollah conflict since 2024, compounded by an economic crisis since 2019). The main research question is: what quantitative effects does war have on the Iranian insurance industry, and what challenges will the fire insurance group face in the post-war years?METHODOLOGY: This applied econometric study used a country‑specific time‑series approach (not panel data, due to non‑uniform conflict timelines). Model 1 (ARIMAX) estimated the direct impact of war (with a one‑year lag) on the fire insurance loss ratio, incorporating autoregressive components to capture persistence in claims. Model 2 (Two‑Stage Least Squares – 2SLS) estimated the indirect impact of war on insurance penetration through GDP, using lagged war as an instrumental variable to address endogeneity concerns. The first stage examined how lagged war affects GDP growth; the second stage used instrumented GDP to explain insurance penetration. Data were extracted from the World Bank Open Data, IMF World Economic Outlook, Swiss Re Sigma Explorer, and national insurance supervisory reports (Central Insurance of Iran, SISC for Syria, NAIU for Ukraine, ICC Lebanon). Diagnostic tests included ADF for stationarity, VIF for multicollinearity, White/Breusch‑Pagan for heteroscedasticity, Durbin‑Watson/Ljung‑Box for autocorrelation. For the 2SLS model, the F‑statistic (weak instruments) and Sargan test (overidentification) were applied. All estimations were performed using EViews. For Iran, a weighted‑average simulation based on the three countries was conducted because final war data for 2025‑2026 have not yet been published; weights were assigned according to economic similarities (Ukraine 0.35, Lebanon 0.30, Syria 0.25).FINDINGS: The direct effect (sum of WAR_t and WAR_{t-1}) increased the loss ratio by 40.6 percentage points in Syria, 38.3 in Ukraine, and 46.9 in Lebanon. The indirect effect (elasticity of insurance penetration to GDP) ranged from 0.072 to 0.093 across the three countries. For Iran, using a weighted average simulation, the fire insurance loss ratio is projected to rise from 86.7% (2024) to 106.7% in the first year of war (2025), and insurance penetration is projected to fall from 2.12% (2024) to 1.08% in the second year of war (2026). Solvency ratios are expected to drop below the regulatory threshold of 100% without government intervention. All models passed diagnostic tests: ADF p<0.05, VIF<5, DW≈2, Sargan p>0.05. The Iranian fire insurance group in 2024, despite having only 3.4% of total premiums, experienced an 83.3% increase in claims paid and a loss ratio of 61.8% (up from 47.2%). In the 12 day war of June 2025, the government pressured insurers – especially Iran Insurance Company – to pay war related claims even for policies without a war clause. This intervention violated technical insurance principles (including the principle of indemnity and the war exclusion) and eroded insurers’ solvency without any pre funded backing.CONCLUSION: War exerts significant upward pressure on loss ratios and downward pressure on insurance penetration through both direct and indirect channels. For Iran, based on the experiences of Syria, Ukraine, and Lebanon, the following policy measures are essential: (1) establishing a national war risk pool (modelled on the UK’s Pool Re and the US TRIA) with mandatory participation of all fire insurers and a government backstop; (2) drafting a transparent and standardized war exclusion clause with clear sub limits, deductibles, and definitions of “war” and “hostile acts”; (3) developing parametric insurance products triggered by satellite confirmed destruction or blast radius to enable rapid payouts without costly on ground assessment; (4) requiring annual stress testing of solvency under low, medium, and high war intensity scenarios (15%, 30%, and 50% increases in loss ratio); (5) building a GIS based accumulation risk database for insured properties; and (6) revising premium tariffs based on regional war risk (border areas, near military facilities, major industrial zones). Without these measures, the bankruptcy of several small and medium sized insurers is likely. Immediate action by the Central Insurance of Iran and the Insurance Research Center is strongly recommended to prevent a systemic collapse of the fire insurance market.

Original Research Paper Economics of finance / insurance

Institutional Synergy between Banking and Insurance in Enhancing Insurance Industry Resilience: A Conceptual Fram

Reza Farid; Shahryar Nessabian; Mahnaz Rabiei

Articles in Press, Accepted Manuscript, Available Online from 17 August 2026

Abstract

Extended AbstractBACKGROUND AND OBJECTIVES:Financial system stability constitutes a fundamental prerequisite for sustainable economic growth and the preservation of public confidence in contemporary economies. Within this architecture, banks and insurance companies function as two interconnected pillars ... Read More Extended AbstractBACKGROUND AND OBJECTIVES:Financial system stability constitutes a fundamental prerequisite for sustainable economic growth and the preservation of public confidence in contemporary economies. Within this architecture, banks and insurance companies function as two interconnected pillars whose complementary roles in resource mobilization, capital allocation, and risk management have become increasingly interdependent. The proliferation of joint investments, cross-ownership structures, and institutional interactions has progressively blurred the traditional boundaries between banking and insurance activities, rendering isolated analyses of either sector both theoretically inadequate and practically misleading. Despite the growing body of literature on banking stability and insurance resilience as separate domains, a significant theoretical and empirical gap persists: no coherent framework has yet been developed to systematically analyze the institutional synergy between banks and insurance companies and its cascading implications for financial system stability. This study addresses this lacuna by deploying the Hybrid Institutional Dynamics Model (HIDM) as a novel analytical framework that simultaneously quantifies three critical institutional mechanisms—governance quality, adaptive learning capacity, and network interactions—to examine how bank-insurance synergy can be leveraged to strengthen insurance industry resilience and financial system stability.METHODS:This study adopts a pragmatic research paradigm and employs a sequential multi-phase quantitative design. The empirical analysis encompasses a purposive sample of nine Iranian banks, stratified by ownership structure into state-owned, privatized, and private categories, alongside their linked insurance companies, covering a twelve-year period from 2014 to 2025, with particular emphasis on the critical years of 2022 and 2025 during which banking crises, economic shocks, and social unrest converged. Data were sourced from audited financial statements, insurance solvency reports, and macroeconomic indicators. The analytical framework is structured around the HIDM model, which operates across three interconnected phases. Phase 1 employs Dynamic Data Envelopment Analysis with a Network Slack-Based Measure approach and carry-over variables to compute dynamic efficiency scores for each financial institution. Phase 2 utilizes a Dynamic Bayesian Panel Probit model with a lagged dependent variable to estimate the non-linear and threshold effects of institutional quality, adaptive learning capacity, and network centrality on the probability of financial distress and institutional synergy. Model estimation was conducted using Markov Chain Monte Carlo simulation with 10,000 iterations in PyMC3, with convergence confirmed through the Gelman-Rubin diagnostic. Phase 3 calibrates an Agent-Based Model with heterogeneous financial institutions and an adaptive regulator, simulating three policy-relevant scenarios—exchange rate shocks, price shocks, and smart supervision—over a 40-quarter horizon.FINDINGS:The empirical results reveal several critical findings. First, a significant institutional efficiency gap exists between state-owned and private financial institutions, with private banks (Eit=0.792) and private insurance companies (Eit=0.753) consistently outperforming their state-owned counterparts by approximately 30 percent. This divergence reflects the adverse effects of government fiscal dominance, weak corporate governance, and political intervention on resource allocation efficiency in both sectors. Second, governance quality emerges as the strongest predictor of institutional synergy, with a coefficient of 0.312 and a Bayes Factor of 10.4, providing decisive statistical evidence for the centrality of transparency, accountability, and regulatory independence in fostering bank-insurance collaboration. Third, adaptive learning capacity exerts both a direct positive effect on institutional synergy (β=0.245) and a significant moderating effect that amplifies the impact of governance quality (Git × Lit interaction β=0.156), functioning as what can be characterized as an "institutional shock absorber." Fourth, network interactions contribute positively to synergy both independently (β=0.198) and through their interaction with governance quality (Git × Nit interaction β=0.127). However, a cautionary finding emerges: dense network connections, in the absence of robust governance mechanisms, can transform from risk-mitigating conduits into channels for accelerated systemic contagion. Fifth, the HIDM model demonstrates 87.9 percent predictive accuracy (AUC=0.879) with a nine-month early warning capability, offering a quantitative, interpretable, and institution-sensitive tool for systemic risk surveillance.CONCLUSION:This study establishes that financial system stability is not the product of isolated bank or insurance company performance, but rather emerges from the quality of institutional interactions between these two pillars. The three mechanisms of governance quality, adaptive learning, and network interactions operate synergistically rather than independently, meaning that deficiencies in any single dimension cannot be fully compensated by strengths in the others. The critical policy implication is that regulators must transcend sectoral approaches and adopt an integrated strategy that simultaneously enhances all three mechanisms across the entire financial system. The establishment of a Financial Stability Coordination Council jointly operated by the Central Bank and the Central Insurance authority, the institutionalization of quarterly Git, Lit, and Nit monitoring as supervisory key performance indicators, and the operational deployment of the HIDM Early Warning System within the financial stability directorate represent urgent strategic imperatives for safeguarding financial system stability.

Original Research Paper Corporate Governance in Insurance Companies

an Indigenous Model for Measuring Disclosure and Transparency in the Iranian Insurance Industry: A Mixed-Method Approach

Davar Mohammadi; Sasan Mehrani; Gholamreza Soleymani Amiri; Asma Hamzeh

Articles in Press, Accepted Manuscript, Available Online from 22 August 2026

Abstract

BACKGROUND AND OBJECTIVES: Disclosure and transparency are fundamental pillars of corporate governance in the insurance industry, playing a critical role in reducing information asymmetry and building public trust (Bushman & Smith, 2003). In Iran, despite Regulation No. 88 (2014), a significant gap ... Read More BACKGROUND AND OBJECTIVES: Disclosure and transparency are fundamental pillars of corporate governance in the insurance industry, playing a critical role in reducing information asymmetry and building public trust (Bushman & Smith, 2003). In Iran, despite Regulation No. 88 (2014), a significant gap persists between formal disclosure mandates and effective, trust-building transparency. Previous studies have narrowly focused on quantitative financial disclosure, neglecting institutional, behavioral, and technological dimensions. The absence of an indigenous, multi-dimensional measurement model for Iran's insurance industry constitutes a key research gap. This study pursues two objectives: (1) designing and validating an indigenous, multi-dimensional model for measuring disclosure and transparency in the Iranian insurance industry, and (2) explaining the structural relationships among its dimensions using a sequential exploratory mixed-method design.METHODS: A sequential exploratory mixed-method approach (Creswell & Plano Clark, 2018) was employed. In the qualitative phase, purposive and snowball sampling selected 12 insurance experts (average 17 years' experience). Semi-structured interviews, designed around seven axes of the initial conceptual model, continued until theoretical saturation (Saunders et al., 2018). Thematic analysis (Braun & Clarke, 2006) using MAXQDA 2020 proceeded through open, axial, and selective coding, yielding 114 open codes, 23 axial categories, and five main themes. Coding reliability was confirmed via Cohen's Kappa (κ = 0.84). In the quantitative phase, a researcher-made 51-item questionnaire, grounded in qualitative findings and measured on a five-point Likert scale, was developed and validated by five experts. The questionnaire was distributed electronically across 25 insurance companies. After iterative purification—removing items with factor loadings below 0.70 and high cross-loadings—the final model comprised nine constructs and 36 items from 108 valid responses. Data were analyzed using PLS-SEM with SmartPLS 4 (Hair et al., 2019), assessing composite reliability (CR), convergent validity (AVE), and discriminant validity (Fornell-Larcker criterion). Hypothesis testing used bootstrapping (5,000 subsamples) at the individual level (n=108); company rankings included 16 companies with at least three respondents (ranging 4–11 responses per company).FINDINGS: Qualitative findings revealed five main themes: (1) Weak regulatory and executive infrastructures—implementation gaps, inadequate information quality, and lack of integrated systems; (2) Corporate governance and accountability gap—divergence between formal structures and actual performance, lack of committee independence, and ownership opacity; (3) Content and substantive disclosure limitations—quantification bias, ESG reporting neglect, and short-termism; (4) Lack of social capital and incentive mechanisms—low professional ethics (20–30%), weak stakeholder demand, and inadequate motivational systems; and (5) Digital void and resistance to innovation—cultural resistance, fragmented information systems, and shortage of specialized human resources. Quantitative results confirmed nine constructs: Corporate Governance (9 items), Voluntary Non-Financial Disclosure (3 items), Digital Maturity (6 items), Technological Disclosure (2 items), Financial Disclosure (2 items), Insurance Information Disclosure (3 items), Digital Customer Experience (2 items), Trust (5 items), and Policyholder Legitimacy/Satisfaction (4 items). All constructs showed satisfactory CR (0.795–0.944) and AVE (0.604–0.849). Discriminant validity was confirmed via the Fornell-Larcker criterion. The model explained 47% of variance in Digital Customer Experience, 30% in Trust, and 63% in Legitimacy/Satisfaction. Of eight hypotheses, four were supported: Corporate Governance (β = 0.281, p < 0.05) and Voluntary Non-Financial Disclosure (β = 0.257, p < 0.05) positively impact Digital Customer Experience; Digital Customer Experience positively impacts Trust (β = 0.545, p < 0.01); and Trust positively impacts Policyholder Legitimacy/Satisfaction (β = 0.793, p < 0.01). Four hypotheses—Digital Maturity, Insurance Information Disclosure, Technological Disclosure, and Financial Disclosure—showed no significant impact. Company rankings placed Khawarmianeh (M = 0.797) and Parsian (M = 0.751) highest, and Moallem (M = −0.692) and Mellat (M = −0.515) lowest. Trust–Legitimacy correlation was strongest (r = 0.922).CONCLUSION: This study presents the first indigenous, multi-dimensional measurement model for disclosure and transparency in Iran's insurance industry, integrating institutional, governance, behavioral, and technological dimensions. Digital Customer Experience emerges as a novel mediating mechanism linking governance and voluntary disclosure to trust and legitimacy. The gap between formal and effective transparency provides a framework for understanding transparency challenges in developing economies. Practically, findings equip the Central Insurance of Iran with evidence-based tools for revising regulations (e.g., Regulation No. 88), designing a national transparency dashboard, and shifting toward effectiveness-oriented supervision. For managers, priorities include strengthening substantive governance, developing voluntary ESG disclosure strategies, and investing in digital customer experience as the primary pathway to trust and legitimacy. Limitations—internal stakeholder focus, cross-sectional design, and context-specificity—suggest future research replicating the model with policyholders, conducting longitudinal studies, and undertaking comparative analyses with other emerging markets.

Original Research Paper New Insurance Technologies

Analyzing the Structure of Iran's Insurance Ecosystem Based on the Viable System Model from an Information Technology Perspective

Ali Rezai; sona bairamzadeh; Abbas ali Hajikarimi; Neda Soltan mohammadi

Articles in Press, Accepted Manuscript, Available Online from 26 August 2026

Abstract

BACKGROUND AND OBJECTIVES: the effective functioning of an ecosystem depends on synergistic collaboration among a diverse set of actors and the interactions between them. The primary objective of the present study is to analyze the current structure of Iran’s insurance ecosystem with a focus on ... Read More BACKGROUND AND OBJECTIVES: the effective functioning of an ecosystem depends on synergistic collaboration among a diverse set of actors and the interactions between them. The primary objective of the present study is to analyze the current structure of Iran’s insurance ecosystem with a focus on information technology and to propose an optimal structure through the application of the Viable System Model (VSM).METHODS: Given that the present study seeks to provide a structural and holistic analysis grounded in the country’s specific socio-economic and political conditions, it adopts, from the perspective of contemporary research methodology, an interpretive paradigm with a qualitative approach within the framework of the Viable System Model (VSM). Data were collected through library research, examination of relevant documents, and interviews conducted with 19 experts from the insurance industry. The data were analyzed in three stages in accordance with the Viable System Model: mapping actors into the five subsystems, identifying missing roles and structural bottlenecks, and designing the proposed model. To ensure the validity and rigor of the study, the trustworthiness criteria—including credibility, transferability, dependability, and confirmability—as well as the evaluation criteria for VSM outputs, were employed.FINDINGS: A structural analysis based on the Viable System Model (VSM) indicates that the primary focus of information technology development has been directed toward operational functions (System 1) and, to some extent, control functions (System 3). In contrast, the vital functions related to coordination, strategic foresight, and high-level policy-making lack sufficient institutional and technological support. This functional imbalance has weakened system viability and reduced the adaptability capacity of the insurance ecosystem in response to environmental changes.At the level of System 2 (coordination), the absence of common data standards, weak inter-organizational interoperability, and the limitation of information systems to post hoc monitoring were identified as the most important sources of institutional misalignment. At the level of System 4 (intelligence and innovation), the lack of a governing body responsible for monitoring technological developments and steering digital innovation has led to fragmented and non-systematic responses to environmental changes. Furthermore, at the level of System 5 (policy and data governance), the multiplicity of decision-making centers and the absence of a coherent framework for information technology and data governance have resulted in weakened systemic identity and increased institutional risks. Accordingly, the study proposes a set of new actors in two categories—“institutional–policy” and “technological–operational”—aimed at strengthening systemic coordination, improving data governance, and enhancing operational efficiency. Overall, the findings suggest that achieving the viability of Iran’s insurance ecosystem requires a shift from a siloed, technology-centric approach to a systemic and integrated approach to information technology.CONCLUSION: The proposed model of the study indicates that the viability of Iran’s insurance ecosystem requires a redesign of both policy and operational roles. The application of the Viable System Model enables the systematic identification of structural gaps and the design of a coherent technological ecosystem, which are elaborated in the article. The novelty of this study lies in its integration of the Viable System Model (VSM) with IT infrastructure in the insurance industry of Iran, a combination that has not previously been explored in the literature. While global research has often applied VSM to different sectors, and other local studies have examined the role of information technology in insurance, no prior work has systematically combined these two perspectives. Given the qualitative and structural nature of the present study, and the application of the Viable System Model (VSM) as a diagnostic–design framework for analyzing the information technology infrastructure of the Iranian insurance industry, it is recommended that future research be developed along several complementary trajectories. These include, the comparative application of the VSM in other technological ecosystems; the examination of inter-level dynamics within the Viable System Model; and the investigation of the role of data governance and information architecture in system viability. Like all research endeavors, the present study is subject to certain limitations. It was conducted using a qualitative approach, drawing upon interviews with 19 experts from the insurance industry, content analysis of official documents, and a review of the relevant scholarly literature. Although this methodological approach provides an in-depth understanding of the dynamics and structural challenges of information technology infrastructure in the Iranian insurance industry, it is not without constraints. Among these is the relatively limited sample size. Nevertheless, this study offers a valuable foundation for future quantitative and mixed-methods research capable of testing and generalizing the findings on a broader scale.

English paper for special issue on "climate change & insurance industry" Risk management in the insurance industry

Integrating Insurance and Risk Management into Climate Change Governance: An SES Framework for Enhancing Resilience

Mohammadreza Farzaneh; Faezeh Banimostafa Arab

Articles in Press, Accepted Manuscript, Available Online from 16 May 2026

Abstract

METHODS: The study used a three-level analytical approach to examine both theoretical and practical aspects of integrating insurance into climate governance. Level I employed Ostrom’s SES framework to design a conceptual model featuring governance structure and six subsystems: economy-wide, public ... Read More METHODS: The study used a three-level analytical approach to examine both theoretical and practical aspects of integrating insurance into climate governance. Level I employed Ostrom’s SES framework to design a conceptual model featuring governance structure and six subsystems: economy-wide, public sector, finance, rural, urban, and social development. The conceptual model helped identify how insurance could facilitate risk management, resource allocation, and adaptation. The dataset was gathered from comprehensive global sources, such as Climate Change Laws of the World, which originally yielded 931 policies in developed countries and 1,321 policies in developing countries, 499 and 429 relevant laws, and 1,520 and 1,015 targets, respectively. Level II consisted of international comparison studies. It used the conceptual model to analyze specific case studies to examine policies, laws, and targets related to insurance, identifying trends and approaches to tailor insurance for climate change adaptation. Level III involved expert feedback gathered using a semi-structured questionnaire sent to 181 professionals, who identified priorities concerning subsystems in governance and provided expertise on climate change, insurance capability, and executing innovative interventions. The methodology combined theoretical foundations, objective evidence, and expert input, which helped localize and globalize efforts to design a conceptual model on climate change.FINDINGS: The conceptual framework placed insurance at the forefront of connectivity within the finance system, facilitating risk transfer and supporting adaptation. Examples across the globe demonstrated that insurance acted as a tool rather than merely a buffer. For instance, Norway’s Natural Damage Insurance Act (1989) required a mandatory pooling mechanism after disasters and provided post-event recovery measures. Flood Re in the UK, launched in 2014, demonstrated how a holistic approach could ensure affordable flood insurance. France, Spain, and Argentina illustrated how comprehensive risk management supported agricultural, land-use change, and forestry programs. National targets in Turkey and Kyrgyzstan showed how policy commitments could be operationalized through insurance platforms to enhance resilience. Experts ranked priorities, emphasizing immediate attention to urban and rural zones, while finance received less priority due to perceived limited immediacy, although its value remained significant. Challenges included lack of climate information, limited access to relevant hazards, poor understanding of insurance project assumptions, underdeveloped reinsurance markets, insufficient eco-insurance options, and limited financing opportunities such as publicly-supported bonds and eco-subsidization programs. Experts recommended greater focus on parametric insurance programs, expanded data pools, more comprehensive mapping, and increased access to innovative financing options. These recommendations highlighted insurance’s role in connecting finance and climate management across multiple governance domain.CONCLUSION: Insurance presented considerable opportunities as a strategic tool for adaptation, mitigation, and DRM in the context of climate change. Successful integration required filling gaps in climate and risk information, designing relevant climate modeling processes, developing insurance products on sectoral and geographic bases, and incorporating available finance options within overall governance systems. Policymakers and stakeholders could achieve optimal results by implementing short-term, localized approaches to complement long-term modifications within finance and insurance systems. This study demonstrated the strategic positioning of insurance to function proactively, rather than solely reactively, within climate change governance processes.

English paper for special issue on "climate change & insurance industry" Risk assessment in insurance

Examining Insurance Mechanisms for Enhancing the Resilience of Iran's Water Resources to Climate Change

Mohammad Ariamanesh; Mansoureh Farzaneh

Articles in Press, Accepted Manuscript, Available Online from 16 May 2026

Abstract

Extended AbstractBackground and ObjectivesIran's water crisis represents a fundamental threat to national security, sustainable development, and social stability. The convergence of climatic changes, unsustainable consumption patterns, and institutional challenges has created an unprecedented water emergency, ... Read More Extended AbstractBackground and ObjectivesIran's water crisis represents a fundamental threat to national security, sustainable development, and social stability. The convergence of climatic changes, unsustainable consumption patterns, and institutional challenges has created an unprecedented water emergency, marked by severe groundwater overdraft and the deterioration of historical water management systems. This research addresses this crisis by developing a comprehensive insurance model specifically designed for Iran's unique hydrological, agricultural, and industrial conditions. The study has three primary objectives: first, to design innovative insurance mechanisms for protecting and revitalizing qanat systems as sustainable groundwater management solutions, directly linking financial protection to maintenance activities; second, to create incentive-based insurance products that promote efficient water consumption across industrial and household sectors through premium structures tied to verified conservation efforts; and third, to establish a robust framework for insuring critical water infrastructure - including treatment plants and dams - against climate-related damages and operational failures, thereby ensuring service continuity. The research particularly focuses on adapting international best practices in water insurance to Iran's specific institutional and environmental context, with special attention to the challenges of data scarcity, limited domestic insurance market capacity, and the critical need for effective public-private partnerships in implementing viable solutions.MethodologyThis study employed an advanced mixed-methods research approach, combining rigorous quantitative analysis with in-depth qualitative assessment through a carefully structured three-phase methodological framework. The initial phase involved a systematic global review of insurance schemes for water-related risks, examining programs from developed nations like Germany's flood insurance system to innovative index-based agricultural insurance in developing countries comparable to Iran, analyzing their structural designs and measurable impacts. The second phase conducted a detailed diagnostic of Iran's water risk landscape, incorporating climate scenario modeling based on WMO (2024) projections, infrastructure vulnerability assessments of key water assets, and comprehensive evaluation of the domestic insurance industry's technical and financial capabilities using Central Insurance of Iran (2022) data, supplemented by case studies of critically endangered qanat systems. The third phase synthesized these global and local findings to develop evidence-based policy recommendations and a detailed implementation roadmap for water insurance in Iran, validated through extensive expert interviews with over 20 stakeholders from insurance companies, water management authorities, agricultural sectors, and environmental organizations, ensuring practical feasibility and contextual appropriateness.FindingsThe research reveals several critical findings with significant policy implications for Iran's water security. First, specialized insurance mechanisms can effectively address multiple dimensions of Iran's water crisis through carefully tailored products: Parametric drought insurance for agriculture using satellite-based vegetation indices and rainfall data can provide timely compensation without costly damage assessment; infrastructure insurance for water treatment plants and transmission networks ensures rapid recovery from climate-related damages; and innovative "qanat conservation insurance" directly links premium discounts and coverage benefits to documented maintenance activities and sustainable operation practices. Second, the study identifies substantial market potential, with water-related insurance potentially expanding to cover 15-20% of Iran's insurance market compared to the current less than 2% coverage for natural disasters, representing significant growth opportunities for insurers while addressing critical national needs. Third, the research demonstrates that insurance products can create powerful economic incentives for water conservation when premium structures are directly linked to verified reductions in water consumption and investments in water-efficient technologies, as evidenced by international examples where industries reduced usage by 20-30% to secure better insurance terms. However, significant implementation barriers remain, including data limitations for accurate risk assessment, technical capacity constraints in insurance risk modeling, regulatory challenges requiring appropriate legal frameworks, and cultural barriers to insurance adoption necessitating comprehensive awareness campaigns.ConclusionThe implementation of a specialized water insurance framework in Iran represents a crucial strategic advancement toward achieving water security and enhancing climate resilience. By providing financial protection for critical water infrastructure, creating economic incentives for sustainable water use through carefully designed premium structures, and supporting the preservation of traditional qanat systems through dedicated conservation insurance, the proposed model can significantly strengthen Iran's adaptive capacity in facing escalating water scarcity challenges. The research demonstrates that success requires strong governmental support through appropriate regulatory frameworks, strategic investment in data infrastructure development, and the establishment of effective public-private partnerships that leverage international expertise and reinsurance capacity. Specifically, we recommend establishing a "Qanat Conservation Insurance Program" with premium subsidies tied to maintenance activities; developing parametric agricultural insurance pilots in high-risk regions; and creating a "Conditional Government Guarantee Fund" to support market development while ensuring adherence to water conservation standards. The proposed model offers a practical and actionable pathway for integrating insurance mechanisms into Iran's national water management strategy, with immediate applications in agricultural, industrial, and municipal water sectors that can contribute substantially to long-term water sustainability, economic stability, and climate resilience. Future implementation should prioritize pilot projects in critically water-stressed regions, coupled with continuous monitoring and evaluation to refine approaches and demonstrate tangible benefits to stakeholders across sectors.

English paper for special issue on "climate change & insurance industry"

A Physics-Informed and Causally-Aware (PICA) Conceptual Framework for Flood Risk Pricing: A Hybrid Deep Learning Approach for Data-Scarce Insurance Markets

Mohammad Reyhani

Articles in Press, Accepted Manuscript, Available Online from 16 May 2026

Abstract

The escalating frequency and severity of hydro-meteorological events, particularly floods driven by climate change, present a profound systemic challenge to the global insurance and reinsurance industry. Traditional actuarial pricing models, predominantly based on Generalized Linear Models (GLMs) and ... Read More The escalating frequency and severity of hydro-meteorological events, particularly floods driven by climate change, present a profound systemic challenge to the global insurance and reinsurance industry. Traditional actuarial pricing models, predominantly based on Generalized Linear Models (GLMs) and historical loss tables, rely on the fundamental assumption of risk stationarity. This assumption has been rendered obsolete by the rapidly changing climate, making legacy models increasingly incapable of capturing the complex, non-linear, and spatially-interconnected nature of modern flood risk. In response to these limitations, standard data-driven Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs), have been proposed as superior alternatives for risk forecasting. However, despite their theoretical promise, these models suffer from two critical flaws that strictly hinder their operational adoption in real-world insurance underwriting. First, they are notoriously "data-hungry," requiring massive, high-resolution datasets of historical losses to generalize effectively. Such datasets are unavailable in most "data-scarce markets," including Iran and much of the Global South, leading to poor model generalization and high predictive uncertainty. Second, their "black-box" nature is fundamentally incompatible with the stringent regulatory and fiduciary requirements of the insurance industry, which demands model transparency, fairness, and explainability (XAI) for pricing decisions. A model that cannot causally explain why it assigns a high premium to a specific property is operationally and ethically unusable. This study addresses this critical research and implementation gap. The primary objective is to design and validate a novel conceptual framework, "PICA" (Physics-Informed and Causally-Aware), for flood risk pricing. This study aims to achieve four specific goals: (1) To formally design the PICA framework by synthesizing state-of-the-art components from diverse AI fields; (2) To specifically address the data-scarcity problem by embedding hydrodynamic physical laws directly into the model’s learning process (Physics-Informed); (3) To resolve the "black-box" problem by integrating causal inference techniques to ensure model outputs are explainable and aligned with real-world causality (Causally-Aware); and (4) To empirically validate the performance of the PICA framework against benchmark DL models, particularly under simulated data-scarce conditions. In this study we used a rigorous multi-phase, mixed-methods research design, combining a systematic review with a high-fidelity quantitative simulation. The first phase, Framework Development, utilized a systematic review following the PRISMA 2020 protocol. Data sources included Scopus, Web of Science, and IEEE Xplore (2020–2025). The analysis focused on "architectural synthesis" of 52 elite articles covering Physics-Informed Neural Networks (PINNs), Graph Neural Networks (GNNs), and Causal Machine Learning. This synthesis directly informed the novel mathematical design of the PICA framework. The second phase, Framework Validation, consisted of a quantitative simulation experiment to test the hypothesis under controlled conditions. A high-fidelity synthetic dataset was generated using the HEC-RAS 2D hydrodynamic model, simulating 50 distinct stochastic flood events over a virtual 100-square-kilometer urban watershed containing 10,000 unique property assets. The primary material was the PICA framework itself, architected as a Hybrid Graph Neural Network (GNN) to explicitly capture the network topology of flood propagation. Two benchmark models were constructed for comparison: (1) a state-of-the-art 2D-Convolutional Neural Network (CNN) and (2) a baseline Random Forest (RF) model. The experimental procedure involved training all models under two distinct scenarios: a "Data-Rich" scenario (using 100% of simulated loss data) and a "Data-Scarce" scenario (using only 20% of available data). Predictive accuracy was evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), with statistical significance determined by the Diebold-Mariano (DM) test. Additionally, Shapley Additive Explanations (SHAP) analysis was conducted to validate model explainability. The Phase 1 review confirmed that while GNNs, PINNs, and Causal ML are individually established, no existing framework integrates all three for actuarial pricing. This led to the formalization of PICA's novel composite loss function: L_total=L_data+λ_phys*L_phys+λ_causal*L_causal. Here, L_phys penalizes violations of the Shallow Water Equations, and L_causal penalizes violations of a predefined causal graph. In Phase 2 validation, quantitative results were significant. In the "Data-Rich" scenario, the PICA framework (RMSE = 0.119) modestly outperformed the CNN (RMSE = 0.137). However, the critical finding emerged in the "Data-Scarce" scenario. The performance of the data-hungry benchmarks collapsed, with the CNN's RMSE deteriorating to 0.482. In stark contrast, the PICA framework leveraged its physics-informed component to maintain robust accuracy (RMSE = 0.165). This represents a 65.8% reduction in prediction error compared to the standard CNN in data-scarce conditions. The Diebold-Mariano test confirmed this superiority was statistically significant (p<0.001). Furthermore, SHAP analysis revealed that PICA correctly identified 'Water Depth' and 'Flow Velocity' as primary risk drivers, whereas the CNN relied on spurious spatial artifacts, confirming PICA effectively resolves the black-box problem. This research successfully validates the PICA framework, demonstrating that the strategic synthesis of physics-informed, causally-aware, and graph-based deep learning provides a superior solution for flood risk pricing in data-scarce markets. The findings provide definitive proof that "teaching" a model the underlying physics of a peril can effectively substitute for the lack of historical data.

English paper for special issue on "climate change & insurance industry" Risk management in the insurance industry

Designing a Hybrid Governance Framework for Building Insurance against Earthquakes and Floods Utilizing Digital Technologies with Private Sector Participation and Smart Monitoring

Mohammad Ariamanesh; shamsolah salami

Articles in Press, Accepted Manuscript, Available Online from 16 May 2026

Abstract

EXTENDED ABSTRACTBACKGROUND AND OBJECTIVESIn recent years, the growing intensity and frequency of natural disasters such as earthquakes and floods have revealed serious weaknesses in traditional building insurance systems. These events not only impose heavy financial burdens on communities but also erode ... Read More EXTENDED ABSTRACTBACKGROUND AND OBJECTIVESIn recent years, the growing intensity and frequency of natural disasters such as earthquakes and floods have revealed serious weaknesses in traditional building insurance systems. These events not only impose heavy financial burdens on communities but also erode public trust in the effectiveness of existing insurance frameworks. Public–private partnership models, although useful in certain contexts, often struggle with inefficiencies, unequal coverage, and delays in responding to the urgent needs of affected populations.This research aims to present a new framework that integrates digital innovation with hybrid governance mechanisms, specifically designed for contexts where societies face seismic and flood risks. In Iran, the devastating earthquakes in Bam and Kermanshah, as well as widespread floods in provinces such as Golestan and Lorestan, have shown that conventional insurance systems are unable to provide timely compensation or restore public confidence. These experiences highlight the urgent need for transformative approaches that can strengthen resilience and ensure fairer, faster, and more transparent insurance coverage.METHODSThe methodological design of this study is based on a comprehensive mixed-methods approach carried out in three interconnected phases. First, a comparative analysis of insurance systems in different countries was conducted to examine governance structures, financial instruments, technological adoption, and private sector participation. Second, multi-criteria decision-making models were applied to incorporate the perspectives of experts from government, industry, and academia. Finally, empirical data from Turkey’s building insurance sector was used to validate the simulations and ensure the robustness of the proposed framework. This approach allowed the study to identify successful practices and adapt them to contexts where earthquake and flood risks are most pressing, without altering the original methodological structure.FINDINGSThe proposed framework consists of four complementary components that together address systemic inefficiencies.• Use of remote sensing and satellite imagery: High-resolution technologies enable rapid and accurate damage assessment. In Iran, delays in estimating losses after the Kermanshah earthquake slowed down compensation processes. By applying these technologies, such delays can be minimized, and trust in insurance systems can be strengthened.• Blockchain-based smart contracts: Automated payment systems enhance transparency and reduce opportunities for fraud. In Iran, policyholders have often expressed frustration with lengthy compensation procedures. Smart contracts can streamline these processes, ensuring that payments are made quickly and reliably, thereby improving satisfaction among insured households.• Conditional government guarantees: A mechanism in which the state covers part of the losses only if buildings comply with seismic resilience standards creates strong incentives for safer construction. In Iran, many damages from earthquakes are linked to poor compliance with building codes. This conditional guarantee would encourage builders and homeowners to adhere to safety standards, reducing long-term risks.• Participatory monitoring platforms: A system that enables real-time interaction among stakeholders—government agencies, insurance companies, and citizens—can improve transparency and accelerate recovery. In Iran, the lack of effective communication between these actors has often slowed reconstruction efforts. A participatory platform would foster collaboration, strengthen social trust, and ensure that rebuilding efforts are more efficient and inclusive.Together, these components form a holistic framework that not only addresses operational inefficiencies but also builds resilience and trust in insurance systems.CONCLUSIONThe findings of this study demonstrate that the strategic integration of digital technologies with innovative governance structures can fundamentally reshape building insurance models in the face of natural disasters. The proposed framework offers practical solutions such as conditional government guarantees, intelligent monitoring platforms, and automated payment systems. These solutions are adaptable to diverse developing economies and can be tailored to the specific needs of each country.For Iran, implementing such a framework could establish a sustainable and resilient insurance system capable of reducing the economic and social pressures caused by earthquakes and floods. By combining technological innovation with governance reforms, policymakers can design insurance regimes that not only compensate for losses but also rebuild public trust and strengthen national resilience.This research provides a strong scientific foundation for decision-makers seeking to create robust insurance systems for natural hazards. It also emphasizes the importance of context-specific strategies, as economic, social, and technological differences significantly influence the success of implementation. In Iran, with its diverse climate and wide range of vulnerable regions, the success of this framework depends on close cooperation between government institutions, the private sector, and civil society. Only through such collaboration can an effective insurance system be built—one that not only addresses immediate losses but also contributes to long-term resilience and social stability.KEYWORDSInsurance; Earthquake; Flood; Governance; Technology; Monitoring

English paper for special issue on "climate change & insurance industry" Risk management in the insurance industry

Climate risk management and the need for a paradigm shift in Iran’s insurance industry

Mahmoud Asad Samani; Sahar Haghnazari

Articles in Press, Accepted Manuscript, Available Online from 16 May 2026

Abstract

BACKGROUND AND OBJECTIVES: Climate change, as a systemic risk, has posed unprecedented challenges to the foundations of the insurance industry. This study aims to examine the bidirectional impacts of climate change include physical (climate-related) and transitional (policy and market-driven) risks, ... Read More BACKGROUND AND OBJECTIVES: Climate change, as a systemic risk, has posed unprecedented challenges to the foundations of the insurance industry. This study aims to examine the bidirectional impacts of climate change include physical (climate-related) and transitional (policy and market-driven) risks, on the insurance sector. Globally, the insurance industry is undergoing a fundamental transformation in response to climate change, with insurers increasingly recognizing it as a material financial and strategic risk. However, responses remain uneven across insurance territories, and practical adaptation is often limited to pricing and reinsurance adjustments rather than long-term resilience planning. In Iran, similar challenges persist. On one hand, the country is prone to a diverse range of natural disasters, predominantly earthquakes and floods. On the other hand, the insurance market still relies on traditional products and pricing models with no effective and specialized climate risk management framework. These dynamics highlight the urgency of adopting forward-looking methodologies that move beyond historical averages and incorporate scenario-based analyses. Moreover, understanding these dual impacts provides a foundation for articulating a comprehensive framework tailored to the structural, regulatory, and market-specific conditions of Iran’s insurance industry. These considerations underscore that without integrating climate science, forward-looking analytics, and adaptive governance structures, insurers will increasingly face solvency pressures and declining risk-bearing capacity. Consequently, redefining strategic priorities has become a prerequisite rather than an option. METHODS: Using a descriptive–analytical approach, this study examines both physical and transitional risks associated with climate change and their implications for the insurance industry. The analysis also draws upon key international regulatory and supervisory frameworks, including the Task Force on Climate-related Financial Disclosures (TCFD) and the Network for Greening the Financial System (NGFS) to assess how strategic adaptation and innovation can enhance the resilience and sustainability of the insurance sector. This methodological approach allows the research to integrate empirical insights with global best practices, thereby ensuring both analytical rigor and contextual relevance. Additionally, the comparative review of established international frameworks provides a benchmark for identifying existing gaps and priority areas for reform within Iran’s insurance ecosystem. The combined use of qualitative assessment and international benchmarks also strengthens the validity of the study’s conclusions, allowing a clearer interpretation of how climate risk frameworks can be operationalized within emerging insurance markets. FINDINGS: Economic losses from natural catastrophes reached USD 318 billion with 14,000 victims in 2024, denoting that 57% of the global losses were uninsured. These are up from USD 303 billion in 2023 and exceed the 10-year average of USD 254 billion. Findings reveal that the sharp increase in natural disaster losses has rendered the insurers’ traditional risk pricing and management models ineffective. Climate change is an enabler for insurers indeed as it can add new business opportunities such as green and parametric products to their portfolios as well. The evidence also indicates widening protection gaps in several regions, demonstrating the limitations of conventional indemnity-based mechanisms. Furthermore, the emergence of new climate-aligned market segments suggests that innovation capacity will increasingly determine the competitiveness and solvency outlook of insurers, particularly in climate-exposed economies. Moreover, the results highlight that insufficient diversification benefits and rising loss correlations further challenge traditional assumptions, reinforcing the necessity for insurers to adopt adaptive and technology-enabled risk management approaches. CONCLUSION: By examining both physical and transitional risks and their diverse impacts on the insurance sector, this study recommends a conceptual framework to illustrate the paradigm shift and explores how the insurance industry can evolve from a passive role as a claims payer to an active, leading role in climate risk management and in facilitating the transition toward a sustainable economy. It is expected that the insurance mechanism should facilitate and incentivize preventive actions too. The study concludes that strategic adaptation and investment in innovation are not only necessary for survival but also essential for the long-term sustainability of Iran’s insurance industry. Hence, a number of strategic approaches are suggested to cope with this situation. Ultimately, strengthening the industry’s analytical capabilities and aligning national practices with global climate-related standards will be essential for achieving this transformation. The proposed framework underscores that proactive engagement, cross-sector cooperation, and innovation-driven policies can significantly enhance the sector’s contribution to national climate resilience. In this context, the study emphasizes that coordinated regulatory reforms, sustained investment in data infrastructure, and capacity-building initiatives will be crucial for enabling a smooth transition toward a resilient, innovation-driven insurance sector.

English paper for special issue on "climate change & insurance industry" Insurance pricing

Pricing catastrophic risk under a hybrid reinsurance treaty: empirical evidence from a peaks-over-threshold approach

Fatemeh Atatalab; Mahboubeh Aalaei

Articles in Press, Accepted Manuscript, Available Online from 16 May 2026

Abstract

Catastrophic risk is fundamentally defined by the simultaneous exposure of a large population to substantial financial or physical losses caused by a single high‑severity peril. Such events—whether natural disasters like earthquakes, floods, and droughts, or human‑induced hazards such as large‑scale ... Read More Catastrophic risk is fundamentally defined by the simultaneous exposure of a large population to substantial financial or physical losses caused by a single high‑severity peril. Such events—whether natural disasters like earthquakes, floods, and droughts, or human‑induced hazards such as large‑scale terrorism—result in severe casualties and extensive infrastructural damage. Iran is recognized as one of the most disaster-prone countries globally, having experienced numerous catastrophic events, particularly seismic disasters, with over 80,000 fatalities reported in the past three decades. The pronounced vulnerability of Iran’s arid and semi‑arid regions highlights the need for rigorous catastrophe risk management and the application of actuarially sound reinsurance pricing methodologies.In the insurance context, extreme events are characterized by their low frequency and high severity. These events can produce losses that exceed the financial capacity of primary insurers, potentially driving them toward insolvency. A catastrophic risk is fundamentally characterized by the simultaneous exposure of a large population to the potential for significant financial or physical loss stemming from a single, high-severity peril. Such events often manifest as natural calamities, including earthquakes, floods, or droughts, or as human-induced hazards such as large-scale terrorism, all leading to extensive loss of life and widespread infrastructure destruction.Therefore, transferring catastrophe (CAT) risk through reinsurance serves as a crucial strategic tool for maintaining solvency and capital stability. The central question addressed in this study is how to accurately determine the CAT reinsurance premium.To this end, the paper proposes a hybrid reinsurance framework that combines Quota‑Share (QS) and Excess‑of‑Loss (XL) contracts for catastrophe coverage. Catastrophe loss data—specifically earthquake fatalities in Iran from 1950 to 2023—are modeled using the Peaks‑Over‑Threshold (POT) method within the framework of Extreme Value Theory (EVT). The parameters of the Generalized Pareto Distribution (GPD) are estimated via Maximum Likelihood Estimation (MLE), and the price is computed based on the Standard Deviation Premium Principle.Sensitivity analysis across multiple threshold percentiles reveals that premium estimation is highly dependent on the selected threshold: higher thresholds consistently yield higher reinsurance premiums, confirming the threshold’s critical role in negotiations between the ceding company and the reinsurer. The proposed QS–XL framework therefore provides an integrated actuarial approach for pricing catastrophic risk in high‑vulnerability regions, offering enhanced financial resilience and underwriting efficiency.The pronounced heavy-tailed characteristic inherent in catastrophe loss distributions significantly dampens the reinsurance companies’ willingness to underwrite direct Catastrophe contracts. To effectively incentivize underwriting participation, the pricing for such contracts typically relies on the Standard Deviation Premium Principle rather than the simpler net premium principle, as this approach better accounts for extreme outcomes. Crucially, to accurately model and capture the aforementioned heavy-tail behavior of catastrophic risk, this paper employs the POT model for fitting the loss data. In this paper, we used the POT model to fit the catastrophic loss data. Using the standard deviation premium principle and considering excess-of-loss reinsurance after quota-share reinsurance contract, we calculated the contract price under different thresholds. To empirically assess the influence of the threshold value on the resultant reinsurance premium, we conducted sensitivity analyses across several predetermined percentile levels for threshold selection. The analysis definitively demonstrated that the chosen threshold value is a critical determinant in establishing the final premium (P). Once an appropriate threshold is established, the Generalized Pareto Distribution (GPD) parameters are subsequently estimated using the Maximum Likelihood Estimation (MLE) method. The findings consistently indicated that selecting a threshold corresponding to a higher data percentile leads to a higher reinsurance premium. Therefore, the selection of the optimal threshold becomes a crucial negotiation point, enabling both the ceding company and the reinsurer to agree on a premium level that is mutually acceptable and statistically justified.Future studies may consider the occurrence of events generating sequences of large claims within a group.The pronounced heavy-tailed characteristic inherent in catastrophe loss distributions significantly dampens the reinsurance companies’ willingness to underwrite direct Catastrophe contracts. To effectively incentivize underwriting participation, the pricing for such contracts typically relies on the Standard Deviation Premium Principle rather than the simpler net premium principle, as this approach better accounts for extreme outcomes. Crucially, to accurately model and capture the aforementioned heavy-tail behavior of catastrophic risk, this paper employs the POT model for fitting the loss data. In this paper, we used the POT model to fit the catastrophic loss data. Using the standard deviation premium principle and considering excess-of-loss reinsurance after quota-share reinsurance contract, we calculated the contract price under different thresholds. To empirically assess the influence of the threshold value on the resultant reinsurance premium, we conducted sensitivity analyses across several predetermined percentile levels for threshold selection. The analysis definitively demonstrated that the chosen threshold value is a critical determinant in establishing the final premium (P). Once an appropriate threshold is established, the Generalized Pareto Distribution (GPD) parameters are subsequently estimated using the Maximum Likelihood Estimation (MLE) method.

English paper for special issue on "climate change & insurance industry" Risk management in the insurance industry

Gap Analysis of Novel Financial, Technological, and Governance Tools for natural disaster Risk Management in Iran

Somaieh Babaei; shamsolah salami; najmeh molaei

Articles in Press, Accepted Manuscript, Available Online from 17 May 2026

Abstract

In recent decades, the global community has witnessed a significant surge in the intensity and frequency of natural disasters, leading to catastrophic human and economic losses. Iran, situated in a region with high seismic and climatic risk, faces unique challenges in mitigating these impacts. Traditional ... Read More In recent decades, the global community has witnessed a significant surge in the intensity and frequency of natural disasters, leading to catastrophic human and economic losses. Iran, situated in a region with high seismic and climatic risk, faces unique challenges in mitigating these impacts. Traditional disaster management frameworks often rely on post-disaster recovery rather than proactive risk reduction. Therefore, the development and implementation of modern financial and insurance models integrated with advanced technologies is a vital necessity. This study was conducted with the primary objective of performing a comprehensive gap analysis and prioritizing disaster risk management strategies in Iran. The research specifically emphasizes the integration of financial, technological, and governance innovations to bridge existing operational gaps. By evaluating the current infrastructure of the Natural Disaster Insurance Fund (NDIF), this study seeks to provide a roadmap for transitioning from traditional reactive paradigms to modern, resilience-based systems.This research is descriptive-analytical, utilizing both qualitative and quantitative data. The research framework was operationalized through a self-administered questionnaire comprising 6 dimensions and 38 initial indicators. The statistical population consisted of 30 experts and specialists from the Natural Disaster Insurance Fund (NDIF), the Climate Research Center, and the International Institute of Earthquake Engineering and Seismology, selected through purposive sampling based on their expertise in insurance, disaster risk management, and modern financial instruments. In the first stage, the content validity of the questionnaire was rigorously evaluated by experts, leading to the refinement of the research tool and a reduction of the questions to 35 final indicators. To ensure internal consistency and reliability of the dimensions, Cronbach's Alpha was calculated, yielding a total value of 0.85. Furthermore, the normality of the data distribution was examined using the Kolmogorov-Smirnov test. Due to the non-normal distribution, the non-parametric Binomial Test was employed to confirm the research hypotheses and determine the suitability of the proposed dimensions. In the subsequent stage, the Full Consistency Method (FUCOM), a modern multi-criteria decision-making tool, was implemented in a Python programming environment. The FUCOM model was selected for its superior ability to manage the subjective judgments of experts with a significantly lower deviation rate compared to traditional methods such as AHP.The empirical results from the Binomial Test confirmed all research hypotheses and proved that technological, financial, and governance dimensions are entirely appropriate and essential platforms for the evolution of natural disaster risk management in Iran. The gap analysis revealed a clear duality: while the national risk management system has reached relative maturity in conventional reactive approaches and physical/structural infrastructures, it faces "critical gaps" in modern domains. Specifically, the findings emphasize the lack of integration of advanced technologies such as Early Warning Systems (EWS) and Big Data analytics. Moreover, the financial sector faces a significant shortage in employing modern risk transfer instruments, including Catastrophe Bonds (Cat Bonds) and blockchain-based smart insurance contracts.The use of the FUCOM model provided precise weighting for the strategic phases. Results indicated that the "pre-occurrence" or "prevention and preparedness" phase holds absolute priority with a dominant weight of 0.648. This finding serves as a scientific mandate for an immediate transition from a "reaction-oriented" (ex-post) system to a "risk-oriented" (ex-ante) framework. Among the specific indicators, "Blue Resilience and Emergency Water Supply" (C1312) emerged as the top priority with a weight of 0.096, followed by "Land Use Planning and Hazard Zoning" (C1311) with a weight of 0.083 in second place. The model demonstrated exceptional mathematical precision with a consistency rate (deviation factor) of 0.0018, confirming the reliability of these prioritizations for policy-making.The integration of these findings led to the development of a comprehensive conceptual model for natural disaster risk management, structured based on the priorities extracted from FUCOM. This study concludes that the traditional and "passive" role of insurance and financial institutions in Iran is no longer sufficient to cope with the challenges posed by increasing natural hazards. There is a strategic necessity to redefine the functional role of the Natural Disaster Insurance Fund and other stakeholders to transform from "mere loss compensators" (passive entities) into "proactive leaders in risk governance" (active entities). This transition requires the institutionalization of technological innovations such as blockchain to guarantee transparency in payments and the utilization of Big Data for real-time risk assessment. Finally, focusing on the pre-disaster phase, as specified by the high weight in the FUCOM analysis, will not only reduce future economic liabilities but also significantly strengthen national resilience and sustainable development.

English paper for special issue on "climate change & insurance industry" Corporate Governance in Insurance Companies

Fiscal Tempest: Climate Risk in Insurance Valuation – Stakeholder Perceptions in Emerging Markets

Keramatollah Heydari Rostami; Yassaman Khalili

Articles in Press, Accepted Manuscript, Available Online from 17 May 2026

Abstract

BACKGROUND AND OBJECTIVES: Climate change poses profound risks to emerging markets, where socioeconomic vulnerabilities and limited adaptive capacity amplify financial instability. The insurance industry, a cornerstone of financial resilience, faces increasing pressure to integrate climate-related risks ... Read More BACKGROUND AND OBJECTIVES: Climate change poses profound risks to emerging markets, where socioeconomic vulnerabilities and limited adaptive capacity amplify financial instability. The insurance industry, a cornerstone of financial resilience, faces increasing pressure to integrate climate-related risks into valuation frameworks. However, data scarcity, regional heterogeneity, and stakeholder ambiguity remain significant barriers. This study introduces a pioneering transdisciplinary framework to integrate climate change risks into insurance valuation models, focusing on stakeholder perceptions and adaptive strategies in Iran—an emerging market characterized by diverse climatic zones (arid, semi-arid, and Mediterranean), water scarcity, and a developing insurance sector. The framework synthesizes environmental economics, risk management, and accounting to quantify climate impacts for actuarial and financial practices. Three core hypotheses are tested:(H1) Localized climate risk models significantly enhance insurance valuation accuracy and premium pricing;(H2) Stakeholder perceptions significantly influence the adoption and effectiveness of climate-responsive insurance products;(H3) Green accounting practices significantly enhance insurers' financial resilience by improving risk management and stakeholder trust.METHODS: The study employs a sequential explanatory mixed-methods design, combining quantitative and qualitative phases. A semi-quantitative questionnaire was administered to 310 stakeholders across three strategically selected regions: Tehran (urban-industrial), Khuzestan (agriculture-oil), and Sistan and Baluchestan (arid-vulnerable). The sample included insurance professionals (29.0%), policymakers (22.6%), sectoral stakeholders (35.5%), and academic/NGO experts (12.9%), selected via stratified purposive sampling (Cochran’s formula; 95% confidence, 5% margin of error; 80% response rate). The questionnaire captured climate risk awareness, perceived likelihood and severity (5- and 7-point Likert scales), adaptation preferences (ranking), and expectations for climate-responsive insurance products. Qualitative data were collected through 30 semi-structured interviews and three participatory workshops (15–20 participants each).Advanced computational modeling included autoregressive neural networks (ANN) with Long Short-Term Memory (LSTM) units—trained on historical data (2000–2025; 70-20-10 split)—to forecast valuation outcomes under RCP4.5 and RCP8.5 scenarios. Fuzzy cognitive maps (FCM) were developed in workshops to model causal relationships among climate risks, adaptation strategies, financial impacts, and social factors. Multi-criteria decision analysis (MCDA) using the Analytic Hierarchy Process ranked adaptation strategies. Quantitative analyses comprised descriptive statistics, exploratory factor analysis (EFA; varimax rotation, KMO=0.82), confirmatory factor analysis (CFA), structural equation modeling (SEM; maximum likelihood), and hierarchical regression. Qualitative data were thematically analyzed using NVivo.FINDINGS: Descriptive results showed high awareness of droughts (mean=4.35/5, 75.2% high) and floods (mean=4.12/5, 68.4% high), particularly in Sistan and Baluchestan (drought mean=4.65) and Tehran (flood mean=4.20). Perceived likelihood and impact were highest for droughts (likelihood mean=6.20/7, impact mean=6.45/7). Parametric insurance ranked as the most preferred adaptation strategy (mean rank=1.85/5, 64.5% top rank), followed by infrastructure upgrades (2.10) and green accounting (2.45). EFA extracted four factors explaining 72.6% of variance: Climate Risk Awareness (34.2%), Adaptation Strategy Preferences (24.1%), Financial Integration (14.3%), and Stakeholder Engagement (10.0%). CFA confirmed model fit (χ²=128.45, df=84, p=0.002, CFI=0.94, RMSEA=0.05, Cronbach’s α≥0.85).SEM results supported all hypotheses: H1 (localized climate risk models → valuation accuracy; β=0.65, p<0.001), H2 (stakeholder perceptions → product adoption; β=0.60, p<0.001), and H3 (green accounting → financial resilience; β=0.58, p<0.001). An exploratory path (stakeholder engagement → product adoption; β=0.45, p<0.001) was also significant. Hierarchical regression showed incremental R² changes of 25% (localized models), 12% (stakeholder perceptions), and 8% (green accounting), with total R²=0.45 (F(3,306)=83.45, p<0.001). MCDA confirmed parametric insurance as the top priority (weight=0.45), excelling in feasibility and risk reduction. The hybrid ANN-FCM framework successfully captured non-linear interactions and stakeholder ambiguities, offering robust projections for premium pricing, reserve allocation, and loss ratios under climate scenarios.CONCLUSION: This study presents a validated, transdisciplinary framework integrating localized climate risk models, stakeholder perceptions, and green accounting to enhance insurance valuation and financial resilience in emerging markets. Parametric insurance—particularly for droughts and floods—emerged as the most viable adaptation tool for data-scarce regions. Policy recommendations include: (1) developing and piloting parametric insurance products using satellite-derived triggers (e.g., soil moisture indices), with initial pilots in Khuzestan and Sistan and Baluchestan; (2) adopting green accounting frameworks aligned with TCFD, supported by actuary training and voluntary CII guidelines; (3) enhancing stakeholder engagement through participatory workshops and a national climate risk dashboard; and (4) fostering InsurTech collaboration for real-time data integration and machine learning-based pricing.Limitations include reliance on simulated extensions due to the absence of disaggregated climate–insurance microdata (addressed via scenario-based stress testing), potential response bias (mitigated by triangulation), and regional focus. Future research should test the framework in other emerging markets (e.g., Sub-Saharan Africa), incorporate additional hazards (sea-level rise), explore blockchain for claims processing, and conduct longitudinal perception studies. Despite these constraints, high reliability (Cronbach’s α≥0.85) and robust model fit (CFI=0.93, RMSEA=0.05) underscore the framework’s validity. This study bridges critical gaps by combining advanced simulation modeling with stakeholder-driven insights, offering actionable strategies for insurers, regulators, and policymakers to navigate the fiscal tempest of climate change in vulnerable regions.

English paper for special issue on "climate change & insurance industry" Insurance Social Studies

The Impact of Environmental Awareness on the Willingness to Purchase Green Insurance in Iran

Mohammad Amin Zandi; Mostafa Mansouri; Mohammad Ehsan Zandi

Articles in Press, Accepted Manuscript, Available Online from 17 May 2026

Abstract

The accelerating global environmental crisis has intensified the necessity of fostering sustainable behavioral patterns within the financial services sector, catalyzing the emergence of *green insurance* as a novel and strategic instrument in the developing world. This study situates itself within that ... Read More The accelerating global environmental crisis has intensified the necessity of fostering sustainable behavioral patterns within the financial services sector, catalyzing the emergence of *green insurance* as a novel and strategic instrument in the developing world. This study situates itself within that paradigm, aiming to elucidate the cognitive and psychological determinants influencing the willingness of Iranian consumers to purchase green insurance products. In a context characterized by severe ecological challenges—ranging from air pollution to water scarcity and biodiversity deterioration—understanding the interplay of individual awareness, concern, attitudes, and institutional trust becomes imperative for advancing sustainable financial systems. Green insurance is both a mechanism of environmental stewardship and a tool of risk management, holding promise for reconciling ecological responsibility with economic prudence. Yet, despite global progress in sustainability discourse, adoption rates remain low in emerging markets where both environmental literacy and trust in institutions continue to evolve. The research was grounded in three theoretical pillars—the *Theory of Planned Behavior (TPB)*, the *Knowledge-Attitude-Behavior (KAB)* model, and the *Environmental Values Framework (EVF)*—which together allow comprehensive examination of the linear and indirect paths among cognitive, affective, and intentional constructs. The study implemented a quantitative cross-sectional survey involving 397 systematically sampled participants representing diverse demographic backgrounds. Methodologically, each construct was operationalized through validated scales: *environmental awareness* through responses reflecting knowledge of environmental problems and their consequences; *environmental concern* through three subdimensions—egoistic, social-altruistic, and biospheric concern—capturing the multi-layered nature of ecological worry; and *environmental attitude* via the New Ecological Paradigm (NEP) scale, assessing broad beliefs regarding humanity’s relationship with nature. *Trust in insurance companies* was examined through perceived reliability, credibility, and integrity, forming the institutional dimension of the analysis. The dependent variable, willingness to purchase green insurance, was assessed via behavioral intention statements, while socioeconomic and experiential covariates—income, education, age, gender, and previous insurance engagement—were employed as controls. Data analysis was performed using *Partial Least Squares Structural Equation Modeling (PLS-SEM)*, a variance-based technique suitable for evaluating complex interdependent relationships and simultaneously assessing both measurement and structural components. The model demonstrated sound psychometric properties and strong fit indices, validating its theoretical robustness. Empirical results confirmed all principal hypotheses at high statistical significance. *Environmental awareness* positively affected purchase willingness, signifying the critical role of ecological knowledge in motivating sustainable insurance behavior. *Environmental concern*, conceptualized as a second-order construct integrating egoistic, altruistic, and biospheric elements, exerted similarly powerful effects; individuals emotionally invested in environmental issues displayed higher readiness to engage with green financial innovations. *Environmental attitude*, as measured through NEP, further strengthened this link, indicating that pro-ecological worldviews substantively shape consumer inclination toward sustainable insurance products. Among the predictors, *trust in insurance institutions* emerged as the most decisive factor. This dimension revealed that perceived credibility and honesty of providers substantially condition consumer intentions, underscoring the essential role of institutional reputation as a catalyst for adoption. Complementary effects were found across control variables: *income* correlated positively with purchase intentions—indicating the relevance of economic feasibility—while *education* amplified acceptance of sustainability-oriented arguments, and *prior insurance experience* increased familiarity and confidence, thereby facilitating openness to green variants. Together, these patterns substantiated the integrated behavioral framework by revealing that purchasing intention results from multilayered cognitive and emotional processes rather than isolated rational evaluation. The research conveys profound implications for both theory and practice. Conceptually, it validates that awareness, though essential, remains insufficient without corresponding emotional and attitudinal engagement. The combination of *environmental concern* and *ecological attitude* transforms awareness into behavioral readiness. Practically, the decisive influence of *institutional trust* reveals the foundational challenge facing Iranian insurers: credibility must be cultivated before green insurance can penetrate mainstream markets. The findings imply strategic recommendations: insurers should design *educational and awareness campaigns* that integrate emotional appeals connecting environmental degradation with personal and social well-being; they must innovate product designs tailored to domestic socioeconomic structures, ensuring accessibility and relevance. Transparent communication and corporate social responsibility initiatives are indispensable in rebuilding confidence among customers who are traditionally skeptical of insurance providers. Equally, partnerships with governmental and environmental organizations could enhance perceived legitimacy and reinforce collective trust. In sum, this study contributes valuable empirical evidence to the burgeoning scholarship on *green finance* and sustainability behavior in developing economies. It advances an integrative model combining cognitive, affective, and institutional dimensions and empirically validates its explanatory power in the Iranian context—a domain hitherto underexplored. The findings indicate that the shift toward sustainable financial systems demands not merely informational elevation but structural reformation in trust and engagement mechanisms. Future research should deepen this exploration by examining the moderating influences of demographic and psychographic variables, validating behavioral outcomes beyond declared intentions through longitudinal or experimental designs, and incorporating social norms and peer-effect dynamics to capture collective behavioral diffusion. Such endeavors would enrich understanding of how environmentally oriented financial innovations evolve under variegated cultural and institutional conditions, progressively shaping the trajectory of sustainable insurance markets across developing societies.

English paper for special issue on "climate change & insurance industry"

QUANTITATIVE ASSESSMENT OF DROUGHT RISK IN IRAN: A COPULA-BASED APPROACH USING SPI AND SPEI INDICES

Mohammad Amin Natanzi; hassan jabari

Articles in Press, Accepted Manuscript, Available Online from 18 May 2026

Abstract

SPEI Drought is one of the most critical climate-related hazards affecting Iran, with substantial consequences for agricultural production, water resources, and socio-economic stability. The predominance of arid and semi-arid climatic conditions, coupled with high interannual variability in precipitation ... Read More SPEI Drought is one of the most critical climate-related hazards affecting Iran, with substantial consequences for agricultural production, water resources, and socio-economic stability. The predominance of arid and semi-arid climatic conditions, coupled with high interannual variability in precipitation and a sustained increase in temperature, has intensified both the frequency and severity of drought events across the country. Traditional drought assessments in Iran have largely relied on single drought indices and linear correlation measures, which are often inadequate for capturing the compound nature of drought processes driven by the simultaneous effects of precipitation deficits and increased atmospheric water demand.In recent decades, temperature-driven evapotranspiration has emerged as a key driver of drought severity under climate change, highlighting the limitations of precipitation-only indicators such as the Standardized Precipitation Index (SPI). In this context, the Standardized Precipitation Evapotranspiration Index (SPEI), which explicitly incorporates potential evapotranspiration, provides a more comprehensive representation of drought conditions. However, understanding drought risk requires not only the analysis of individual indices but also the characterization of their joint behavior and dependence structure.The primary objective of this study is to provide a probabilistic and multivariate assessment of drought risk across major climatic regions of Iran by jointly analyzing SPI and SPEI using copula theory. By modeling the dependence structure between these indices, the study aims to capture nonlinear and asymmetric relationships, particularly tail dependence associated with extreme drought events. In addition to advancing methodological understanding, the study seeks to demonstrate the relevance of copula-based drought modeling for practical applications in drought risk management, climate adaptation planning, and the conceptual development of index-based insurance schemes.METHODS: This study is based on long-term climatic observations from synoptic meteorological stations distributed across Iran, covering the period 1981–2020. Monthly precipitation and temperature data were used to compute SPI and SPEI at multiple accumulation time scales, enabling the assessment of both short-term and long-term drought conditions. The calculation of SPEI incorporated potential evapotranspiration to account for the influence of rising temperatures on drought intensity.Marginal probability distributions of SPI and SPEI were fitted using the Maximum Likelihood Estimation (MLE) method. Several copula families—including Gaussian, Student-t, Clayton, and Gumbel—were evaluated to model the dependence structure between the two indices. These copulas were selected to represent a wide range of dependence behaviors, including symmetric dependence, tail dependence, and asymmetric lower- or upper-tail dependence. Model selection was conducted using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and goodness-of-fit tests.Lower- and upper-tail dependence coefficients were estimated to quantify the likelihood of concurrent extreme drought conditions. Joint probabilities and return periods of severe and extreme drought events were derived from the fitted copula models for different climatic regions of Iran. These probabilistic measures were further examined in relation to historical drought occurrences to ensure consistency with observed drought behavior FINDINGS: The results reveal pronounced spatial heterogeneity in drought characteristics and dependence structures across Iran. Arid and semi-arid regions, particularly the Central Plateau and eastern parts of the country, exhibit strong lower-tail dependence between SPI and SPEI, indicating a high likelihood of concurrent extreme drought conditions driven by both precipitation deficits and elevated evaporative demand. In these regions, the Clayton copula consistently provided the best fit, reflecting its ability to capture asymmetric lower-tail dependence relevant to severe drought events.In contrast, humid regions along the Caspian coast display weaker dependence structures and longer drought return periods, with limited evidence of strong tail dependence. Transitional climatic zones were better represented by the Student-t copula, which captures symmetric tail dependence and intermediate drought behavior. Estimated joint probabilities and return periods indicate that severe compound droughts recur approximately every 14–20 years in arid regions, while recurrence intervals exceed 50 years in northern humid areas. These findings are consistent with documented historical drought events in Iran, including major drought episodes in the early 1970s, late 1990s, and late 2010CONCLUSION: The findings contribute to a probabilistic characterization of drought risk in Iran and provide a quantitative basis for understanding compound drought behavior. While this study does not develop a full actuarial pricing framework, the results may support future applications in drought risk management and the conceptual design of index-based insurance and climate adaptation strategiesBy integrating multivariate drought indices with copula-based dependence modeling, this study demonstrates the importance of moving beyond univariate and correlation-based approaches in drought risk assessment. The results highlight the increasing role of temperature-driven evapotranspiration under climate change and underscore the value of SPEI-based analyses in warming environments. Overall, the proposed framework offers a robust, flexible, and policy-relevant tool for assessing drought risk in Iran and provides a scientific foundation for future efforts aimed at enhancing climate resilience, improving agricultural risk management, and supporting the development of innovative risk transfer mechanisms

Journal Date created	2012
Google h-index	7
Google i10 h-index	4
Google Total Citations	514
Issues per year	4
Volumes published	14
Issues published	52
Articles published	357
Indexing databases	21
Acceptance rate	11%
Average First Action	1 Days
Average Review Date	29 Days
Average Time to Accept	65 Days

Volume 15 (2026)

Volume 14 (2025)

Volume 13 (2024)

Volume 12 (2023)

Volume 11 (2022)

Volume 10 (2021)

Volume 9 (2020)

Volume 8 (2019)

Volume 7 (2018)

Volume 6 (2017)

Volume 5 (2016)

Volume 4 (2015)

Volume 3 (2014)

Volume 2 (2013)

Volume 1 (2012)

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