Original Research Paper
New Insurance Technologies
Fatemeh Najibzadeh Vamegh Abadi; Sayyed Mohammad Hadi Ghabooli Dorafshan; Hosein Naseri Moghaddam
Abstract
BACKGROUND AND OBJECTIVES: The expansion of artificial intelligence (AI) and Internet of Things (IoT) systems, characterized by data-dependence, learning capabilities, continuous updating, and multi-actor interaction across supply and operational chains, has challenged classical models of liability allocation ...
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BACKGROUND AND OBJECTIVES: The expansion of artificial intelligence (AI) and Internet of Things (IoT) systems, characterized by data-dependence, learning capabilities, continuous updating, and multi-actor interaction across supply and operational chains, has challenged classical models of liability allocation and the logic of insurability with new forms of uncertainty. Because AI and IoT systems often evolve after deployment, operate through interconnected hardware and software layers, and distribute control across manufacturers, developers, deployers, operators, importers, and service providers, they complicate the identification of the legally relevant act, the responsible person, and the causal path linking malfunction to damage. These characteristics challenge the assumptions on which traditional civil liability and insurance have long relied, namely relative stability of product configuration, clearer boundaries between product and service, and a more direct relationship between conduct, fault, defect, and harm. In the European Union, the consolidation of risk-based regulation under the Artificial Intelligence Act and the redesigning of product-defect liability under the revised Product Liability Framework have strengthened the capacity to translate technological risk into legally auditable and actuarially priceable risk. Yet this transformation is not linear. The same reforms that clarify certain duties may also broaden the circle of potentially liable actors, expand recoverable damage, and intensify disputes over evidentiary access, causation, and allocation of loss in complex socio-technical ecosystems. This article therefore assesses the extent to which the emerging legislative architecture enhances liability predictability and supports the insurability of AI-IoT-related risks, while identifying the points at which interpretive and enforcement heterogeneity, together with aggregated and correlated risks, may undermine insurability. The article also asks whether the EU’s current model merely improves ex ante risk governance, or whether it can also sustain ex post compensation through insurance markets under the conditions of technological opacity, cross-border litigation, and systemic exposure.METHODs: Adopting a legislative-interpretive approach and a normative analysis of the core EU instruments, the study proceeds along three axes: (1) the construction of risk and compliance obligations in the AI Act; (2) the redefinition of “product,” “liable person,” the scope of compensable harm, and related evidentiary mechanisms in the revised Product Liability regime; and (3) the implications of the absence of an EU-wide harmonized layer of proof in fault-based liability following the abandonment of the AI liability initiative. On the first axis, the analysis focuses on how the AI Act turns technical and organizational requirements into legally reviewable obligations, including data governance, documentation, logging, transparency, human oversight, post-market monitoring, and traceability across the system lifecycle. On the second axis, it examines how the revised Product Liability Framework accommodates software-intensive and update-dependent technologies by extending the concept of product, broadening the category of accountable economic operators, and facilitating the production and disclosure of evidence in disputes involving technical opacity. On the third axis, it evaluates the legal and insurance consequences of leaving fault-based claims subject to divergent national procedural and evidentiary regimes, especially regarding standards of proof, access to information, judicial timing, and causation analysis. These legal elements are then mapped onto the technical variables of insurance underwriting, including data and model auditability, the control of moral hazard and adverse selection, aggregation-risk management, premium differentiation, the design of compliance-based policy conditions, deductibles, limits of liability, exclusions, and the structuring of reinsurance responses to low-frequency and high-severity events.FINDINGS: Clearer liability boundaries and measurable obligations, particularly in high-risk domains, enable insurers to rely more heavily on external compliance benchmarks, reduce dispute costs over causation and fault, and support more realistic risk-based pricing and contractual design. The AI Act contributes to this outcome by transforming broad expectations of safe and trustworthy AI into verifiable legal duties that can function as underwriting signals, policy conditions, and claims-handling reference points. The revised Product Liability Regime likewise improves legal intelligibility by clarifying the status of software and digitally integrated systems, by extending possible liability beyond the narrow figure of the traditional manufacturer, and by facilitating evidentiary access in contexts where defect and causation would otherwise remain difficult to establish. From an insurance perspective, these developments improve observability, reduce some information asymmetries, and create a more structured basis for calibrating premiums, exclusions, deductibles, and coverage triggers. Nevertheless, the findings also show that the same architecture generates new pressures on insurability. The expanded set of potentially liable actors, the rise of multi-defendant litigation, and the difficulty of quantifying certain data-related and non-pecuniary losses, combined with divergence in evidentiary standards and adjudicatory timelines across Member States, introduce divergent legal risk into pricing functions and place market capacity under strain, especially in low-frequency, high-severity, and highly correlated scenarios such as update shocks, security incidents, and systemic failures. This is particularly important where a single defect, update, model error, or cyber event may affect large numbers of devices or users simultaneously, thereby turning what appears to be isolated liability into accumulated exposure. The absence of a harmonized evidentiary layer for fault-based liability means that legal uncertainty remains unevenly distributed across jurisdictions, increasing reserve volatility, claims administration costs, and the difficulty of maintaining consistent underwriting standards across the internal market.CONCLUSION: Effective insurance responses to AI-IoT risks depend on three concurrent linkages: (1) lifecycle traceability and auditability of data and models; (2) tying coverage to compliance-based conditions and robust incident-logging and event-recording obligations; and (3) a minimum degree of interpretive and institutional coordination to reduce evidentiary fragmentation and cross-border transaction costs. Absent these linkages, fault-shifting within ecosystemic chains and correlated risks may destabilize premiums and constrain market capacity. Accordingly, standardizing baseline disclosure and event-recording requirements, periodically reviewing regulatory regimes against insurability metrics, and strengthening reinsurance mechanisms and risk pools are central to sustaining insurability and ensuring continuity of compensation in an evolving technological environment.
Original Research Paper
New Insurance Technologies
Leili Niakan
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 ...
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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 at 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 sample 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 reveals the progress made in individual maturity dimensions. Indicators related to "customer online trust" experienced marked improvement in the majority of companies, implying 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 disclosed no significant correlation between maturity levels and company size—measured by the 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. Firstly, it underscores the need for insurance companies to develop and implement a comprehensive big data strategy aligned with business objectives and customer needs. Secondly, investing in the education, training, and development of specialized human resources in big data analytics is essential to sustaining long-term progress. Thirdly, 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
Economics of finance / insurance
Elham Alizadeh; Jafar Haghighat; Hossein Asgharpur; Zahra Karimitakanlu
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 ...
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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 every 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 (ROE). 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 ignoring 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 ought to 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 (Persian Solar Hijri Year) 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 (Persian Year) to 1402:8 (Persian Year). 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 (Persian Solar Hijri Year), 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.
Original Research Paper
Marketing and Sales
Peyman Sedaghat; Ehsan Abedi; Hamidreza Yazdani; Saeid Sehat
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, ...
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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 with the Glaserian approach, 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 and categorized 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. At the center of this framework lies the core phenomenon of insurance branding, which is conceptualized as a multidimensional process resting on four key constructs: (1) aligning human resources with the brand vision through meritocracy, training, and empowerment; (2) defining and reinforcing brand identity and personality so that brand elements—such as name, logo, slogan, and colors—communicate attributes like honesty, reliability, and strength; (3) achieving brand consistency and positioning in customers minds by fostering a brand-oriented culture across all organizational levels; and (4) developing and implementing a comprehensive service marketing mix tailored to insurance, including product design, pricing strategies, promotional activities, distribution channels, processes, and physical evidence.CONCLUSION: The results indicate that adopting appropriate strategies in insurance branding can lead to positive economic outcomes such as enhancing 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 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
Future research in the insurance industry
Mohammad Javad Zare Bahnamiri; Mohammad Hasan Maleki; Amirabbas Salemi
Abstract
BACKGROUND AND OBJECTIVES: In recent decades, the Takaful industry has experienced a 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 ...
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BACKGROUND AND OBJECTIVES: In recent decades, the Takaful industry has experienced a 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 future-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 depolyed. The number of experts participating in this study was 15, considered appropriate and acceptable for judgment-based and 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, the 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, the 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, the 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
Risk management in the insurance industry
Majid Benvidi; Seyed Mohammadreza Miri Lavasani; Ghodratollah Emamverdi; Kambiz Peykarjou
Abstract
BACKGROUND and OBJECTIVES: The capacity to retain underwriting risk and the continuous provision of strategic mechanisms to augment this capacity are essential to the operational stability, long-term survival, and financial resilience of insurance institutions. A primary and globally recognized strategy ...
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BACKGROUND and OBJECTIVES: The capacity to retain underwriting risk and the continuous provision of strategic mechanisms to augment this capacity are 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. In addition 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 policy 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 the 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.METHODS: This research adopts 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 (Persian Year)). 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 volatile 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 (Persian Year). 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 (Persian Year) 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.
