Original Research Paper
Insurance pricing
Amirmohammad Norouzi; Mojtaba Ranjbar; Saman Vahabi; Mitra Ghanbarzadeh
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 ...
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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
Zeinab Jafarzadeh; Narges Mirehi; Effat Golpar Raboky
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 ...
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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
Shabnam Refoua; Hosseinali Bakhtiar Nasrabadi
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 ...
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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
Mohsen Gharahkhani; Marjan Gharahkhani; Feryal Farakesh
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 ...
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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
Hamid Afkar
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 ...
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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
Mehdi Haghighikafash; Vahid Khashei; Shahram Khalil Nezhad; Vahideh Nourani
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 ...
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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.
