Examining Different Models for Rating Insurance Companies and Selecting an Appropriate Model for Implementation in Iran’s Insurance Industry

Authors

1 . Associate Professor of Economics, Faculty of Economics and Administrative Sciences, University of Qom, Qom, Iran

2 . Master’s student in Islamic Banking, University of Qom, Qom, Iran

3 . Assistant Professor, Department of Modern Insurance Technologies, Insurance Research Center, Tehran, Iran.

Abstract

Extended Abstract
 
Introduction and Objectives: The insurance sector, as a cornerstone of a nation’s financial system, is essential for fostering economic stability, mitigating uncertainties, and supporting investment and production activities. In recent years, the rapid expansion of private insurance companies and intensifying competition have underscored the need for a precise, reliable, and data-driven rating system. Currently, evaluation processes are fragmented and limited in scope, impeding informed decision-making by policyholders, regulatory authorities, and policymakers. This study aims to design a comprehensive and localized rating model that integrates financial, operational, and human capital indicators with modern multi-criteria decision-making (MCDM) techniques. By filling a critical gap in the literature on Iran’s insurance sector, the proposed model enhances transparency, promotes competition, and improves efficiency. Given the emphasis in national upstream documents—including the General Policies of the Resistance Economy and principles of Islamic Finance—developing such a performance evaluation model contributes to financial health, mitigates systemic risks, and strengthens insurance functions within an Islamic economic framework.
Research Methodology: A pragmatic, mixed-method approach was adopted following Saunders’ research onion model. Initially, 22 performance indicators were extracted from domestic and international literature, regulatory reports, and the Central Insurance of Iran’s statistical yearbook, across financial, operational, and growth & learning/customer-oriented dimensions. The fuzzy Delphi method, informed by 25 industry experts, refined the indicators to 17 based on the fuzzy threshold. In the second stage, indicator weights were computed using the Ordinal Priority Approach (OPA) implemented in LINGO software, ensuring consistency based on expert preferences. In the third stage, data from 28 insurance companies—including state-owned, private, and free-zone entities—were collected. Three MCDM methods (PROMETHEE II, SECA, and MARCOS) were applied, reflecting diverse analytical foundations (pairwise comparison, outranking, distance from ideal and anti-ideal solutions). The geometric mean of the three methods’ outputs was used to derive a stable and consolidated ranking.
Results: The fuzzy Delphi process eliminated five indicators, leaving 17 as the foundation of the rating model. OPA weights identified “Loss Ratio” as the most critical indicator, followed by “Number of Issued Policies,” “Market Share of Premiums,” and “Growth Rate of Issued Premiums,” highlighting that claims management, sales efficiency, and competitive positioning are decisive for company performance. Ranking results differed among the MCDM methods: PROMETHEE II ranked Iran, Asia, and Dana insurance companies highest; SECA ranked Iran, Alborz, and Parsian at the top; MARCOS highlighted Ta’avon, Hekmat Saba, and Middle East companies. The geometric mean reconciled discrepancies, yielding a final ranking with Iran Insurance Company first, followed by Alborz, Parsian, Dana, and Pasargad. Smaller and newer companies exhibited weaker performance, particularly in loss ratio and market share indicators.
Discussion and Conclusion: The findings indicate that a multi-dimensional, comprehensive rating approach offers a more accurate assessment of insurance company performance. Loss ratio emerges as a pivotal criterion, emphasizing efficiency in claims management. Market share and policy issuance indicators underscore the importance of marketing, sales networks, and portfolio management. Divergence across MCDM methods suggests that reliance on a single method can lead to biased conclusions; combining multiple approaches and employing the geometric mean ensures stability and reliability. From a policy perspective, this framework assists the Central Insurance of Iran in monitoring solvency, identifying high-risk companies, and fostering healthy market competition. For companies, it provides a roadmap to assess strengths, address weaknesses, and enhance risk management. Aligned with Islamic economic principles emphasizing transparency, justice, and efficiency, this model promotes public trust and financial stability. Future studies should incorporate policyholder satisfaction, digital service quality, corporate governance, and innovation, leveraging data-driven techniques such as machine learning to explore dynamic performance trends.
Acknowledgments: The authors express sincere gratitude to the editors and reviewers of the Journal of Islamic Economic Essays for their valuable guidance in enhancing the scientific quality of this article.
Conflict of Interest: The authors declare no scientific, financial, or organizational conflicts of interest.

Keywords

References

منابع
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