Abstract

Corporate governance in insurance institutions is of particular importance because of the industry’s role in risk management, the fulfillment of long-term contractual obligations, financial solvency, and the protection of policyholders and other stakeholders. In Iran, Regulation No. 93 of the High Council of Insurance provides the principal framework for corporate governance in insurance institutions. Although the Regulation has established formal and binding governance requirements, several years of implementation have raised questions concerning its practical effectiveness, implementation challenges, regulatory coherence, and the adequacy of its current architecture in addressing the evolving needs of the insurance industry. Against this background, the present study aims to identify and analyze the implementation challenges of Regulation No. 93 and to develop practical and regulatory recommendations for improving its effectiveness.

The study is applied and policy-oriented in terms of purpose and descriptive-analytical in terms of nature. It adopts a mixed-methods approach with a quantitative-dominant design and was conducted at a single point in time. The research consists of three complementary components. First, Regulation No. 93, its related implementing instructions, corporate governance requirements issued by the Securities and Exchange Organization (SEO), and selected international corporate governance frameworks were examined through documentary and comparative analysis. Second, a structured questionnaire containing closed-ended and open-ended questions was used to assess the implementation of the Regulation, identify implementation challenges and barriers, and evaluate proposed reforms. Following the removal of one pilot record, 49 valid questionnaires were included in the final analysis. Third, participants’ open-ended responses were analyzed qualitatively and integrated with the quantitative findings, regulatory analysis, and comparative evidence.

The findings indicate that Regulation No. 93 has played an important role in establishing formal corporate governance structures within Iranian insurance institutions. However, a meaningful gap remains between formal structural implementation and actual effectiveness. The overall mean assessment of the implementation of the Regulation’s requirements was 4.77 out of 5. Nevertheless, further analysis indicates that the existence of boards, specialized committees, control functions, and formal governance arrangements does not necessarily ensure effective decision-making, genuine independence, appropriate reporting relationships, professional competence, or meaningful influence on organizational decisions.

The overall mean of the assessed implementation challenges was 3.24 out of 5. The most prominent perceived challenge was the overlap or conflict between certain requirements of Regulation No. 93 and the corporate governance requirements of the SEO applicable to listed insurance companies. Other major challenges included the outdated nature of certain regulatory provisions, the absence of standardized indicators for assessing corporate governance quality, insufficient continuous professional training, inadequate implementing instructions, ambiguity in interpreting certain requirements, and weaknesses in enforcement mechanisms.

The qualitative findings broadly corroborated the quantitative results. Regulatory inconsistency, the structure and functioning of governance committees and control functions, regulatory ambiguity, formalistic implementation, insufficient proportionality of requirements to the size and characteristics of institutions, weaknesses in supervision and effectiveness indicators, limited practical independence of control functions, shortages in professional expertise and training, risk management and solvency issues, and emerging technology and data-related risks were among the principal themes identified in the open-ended responses. The integration of quantitative and qualitative findings suggests that the central issue is not simply the absence of formal governance structures or regulations, but rather the quality of implementation, regulatory coordination, practical independence, and effectiveness of existing governance mechanisms.

Based on the findings, the study proposes that the reform of Regulation No. 93 should extend beyond isolated amendments and should involve a broader redesign of the regulatory architecture of corporate governance in the insurance industry. Key recommendations include strengthening the independence and accountability of boards, clarifying the respective responsibilities of boards and executive management, redesigning the position and reporting lines of control functions, strengthening professional competency requirements, completing specialized implementing instructions, establishing a systematic framework for assessing corporate governance effectiveness, strengthening the integration of governance with risk management and solvency, applying the principle of proportionality, and improving coordination between the Central Insurance of Iran and the SEO. The proposed framework also includes standardized annual corporate governance reporting and measurable indicators for assessing the performance of governance bodies and the effectiveness of implementation.

Overall, the study suggests that improving corporate governance in the Iranian insurance industry depends less on simply increasing the number of regulatory requirements and more on establishing a coherent, coordinated, measurable, and proportionate governance framework. Such a framework should clearly connect primary regulation, implementing instruments, governance structures, risk management, supervision, reporting, and effectiveness assessment, while allowing the regulatory system to adapt to institutional characteristics and future developments in the insurance industry.

Keywords: Corporate governance; insurance institutions; Regulation No. 93; regulatory coherence; governance effectiveness; risk management; solvency; regulatory reform; proportionality; internal control functions

Abstract

The insurance industry plays a significant role in the economic development of countries due to its critical function in risk management and in transforming uncertainty into manageable financial risk. The fundamental nature of insurance is based on risk sharing and mitigating losses arising from uncertain events. Consequently, decision-making in this sector requires strong support from scientific and data-driven analyses in order to achieve effective and sustainable outcomes.

This study aims to identify and explain the mechanisms linking the insurance industry with macroeconomic variables and to address the central question of how developments in the national economy influence the performance of the insurance sector and the nature of these relationships.

The research methodology is based on descriptive and inferential statistical analysis, as well as econometric modeling, particularly macroeconomic models in which the insurance sector is explicitly incorporated. Various macroeconomic modeling approaches were examined, including the IS–LM framework, real business cycle models, small-scale and large-scale macroeconomic models, computable general equilibrium (CGE) models, dynamic stochastic general equilibrium (DSGE) models, monetary disequilibrium models, vector autoregressive (VAR) models, stock-flow models, and satellite models.

Based on the results of the empirical analyses and modeling, insurance lines can be categorized into three groups according to their sensitivity to macroeconomic variables. The first group includes insurance lines influenced primarily by asset values and capital accumulation, such as third-party liability insurance, general liability insurance, fire insurance, and engineering insurance. The second group consists of insurance products strongly affected by inflation, including life insurance (life and savings) and health insurance. The third group includes insurance lines that depend largely on the level of economic activity, such as cargo insurance.

The findings indicate that although non-life insurance lines demonstrate relative short-term stability, they may experience gradual erosion of insurance capital and a steady decline in real premium values in the long run. This highlights the need for revising insurance product design, adjusting premium-setting mechanisms, and developing innovative risk management instruments. The second and third categories of insurance exhibit greater vulnerability to inflationary pressures. In particular, life and health insurance face declining demand and reduced attractiveness for policyholders, while cargo and travel insurance may contract due to reduced production levels and lower volumes of economic exchange.

Based on these findings, the study provides policy recommendations aimed at strengthening life insurance, health insurance, and non-life insurance markets under changing macroeconomic conditions.

Keywords: Insurance industry; macroeconomic variables; econometric modeling; inflation effects; insurance demand; insurance market indicators; forecasting

Abstract

The insurance industry encompasses a wide range of business lines—from health and life insurance to property and liability insurance—and consequently manages highly diverse and heterogeneous data. The presence of numerous insurance companies (approximately 38), along with key related entities such as the Central Insurance of the Islamic Republic of Iran, the Fund for Compensation of Bodily Injuries, loss adjustment firms, emerging startups, entrepreneurial ventures, and external institutions including the Law Enforcement Force (NAJA) and the Ministry of Health, has made information exchange a critical and complex challenge within the sector.

Data interoperability, as a fundamental pillar of effective data governance, requires the adoption of a standardized canonical data model to ensure integrity, consistency, and efficiency in information exchange. In the absence of a unified and standardized data model, the industry inevitably faces redundancy, inefficiencies, increased operational costs, and fragmented data ecosystems. Accordingly, this research project aims to design a localized standard data model tailored to the specific needs of the Iranian insurance industry.

Many countries have developed standardized data models to facilitate structured data exchange among insurance companies, startups, and external institutions. Prominent examples include frameworks developed by organizations such as ACORD, OMG, and IBM.

In this study, the selection of an international reference model was conducted through expert interviews and consensus-building among industry specialists. Based on predefined evaluation criteria, one of the leading international frameworks (including ACORD and OMG) was selected for further assessment. Following theoretical saturation and expert consensus, the ACORD standard was chosen as the reference framework for developing the national conceptual insurance data model. In parallel, industry-specific requirements were systematically identified through structured expert consultations.

Subsequently, taking into account the intended architecture of the national conceptual data model, the selected framework was localized and adapted to align with domestic regulatory, operational, and structural requirements. Among the evaluated alternatives, ACORD demonstrated the highest level of cross-line comprehensiveness—identified during expert sessions as the most critical evaluation criterion.

This report presents conceptual data tables for selected insurance lines, including property and liability, life, and health insurance, along with their interrelationships. Finally, practical recommendations are provided for the implementation, governance, maintenance, updating, and continuous oversight of the proposed model.

Keywords: Insurance industry, Data standardization, Canonical data model, Conceptual data model, Insurance data model, ACORD, Data governance, Information exchange, Insurance digital transformation, Insurance ecosystem, Industry standards, Iran

Abstract

The long-term nature of life insurance contracts exposes both policyholders and insurers to significant inflationary risks. For policyholders, inflation erodes the real value and utility of insured capital and maturity reserves, often driving them toward tangible assets like real estate, gold, and foreign currency. Consequently, inflation acts as a fundamental systemic risk within the life insurance sector, necessitating strategic adjustments in product design and investment portfolio management. This research investigates the impact of inflation on the life insurance industry, with a primary focus on the Iranian market. It aims to evaluate current risk-mitigation strategies, identify global best practices for stimulating demand under inflationary pressure, and propose a localized framework for implementation. The study employs a comparative analysis of inflationary impacts on life insurance in Iran and selected international markets. Through a critical assessment of current industrial practices, it synthesizes global innovative solutions and evaluates their feasibility for the Iranian socio-economic landscape. The research highlights existing limitations in current hedging strategies and identifies key structural gaps. By prioritizing evidence-based solutions, this project provides a detailed implementation framework designed to enhance the resilience of Iran’s life insurance products and stabilize long-term demand. The findings offer policymakers and industry stakeholders actionable recommendations to mitigate inflationary erosion and foster sustainable growth within the sector.

Keywords: Financial Resilience, Inflationary Risk, Insurance Industry, Investment Strategy, Iran, Life Insurance

Abstract

Given the increasing complexity of the insurance market, the growth in data volume, the rapid development of digital technologies, and the limitations of traditional ex-post supervisory approaches, the need to redesign the supervisory system of the insurance sector toward an intelligent, preventive, and risk-based model was defined as the core problem of this research. Accordingly, the project was conducted with the objective of developing a roadmap for the implementation of intelligent supervision at Central Insurance of the Islamic Republic of Iran, adopting an applied and policy-oriented approach. The research methodology combined literature and document review, comparative analysis of international experiences, in-depth expert interviews, and action research within real supervisory processes. The project phases included extraction of the theoretical framework and key success factors; assessment and diagnosis of the current state across data, organizational structure, regulatory framework, and supervisory processes; design of the conceptual model for intelligent supervision, supervisory data and analytics architecture, performance indicators, and transition plan; and ultimately the development of a phased implementation roadmap. The findings indicate that the gradual deployment of intelligent supervision can significantly reduce the time lag between violation occurrence and detection, shift supervision from a reactive to a preventive paradigm, enhance market transparency and risk management, and facilitate sustainable institutional transformation within the national insurance supervisory system.

Keywords: Intelligent Insurance Supervision, Risk-Based Supervision, Digital Regulatory Transformation, Supervisory Data Architecture, Financial Supervision Roadmap

Abstract

Since 2007, Iran insurance industry has seriously suffered from United Nations Security Council (UNSC) sanction resolution 1747, European Union (EU) related regulations and unilateral measures by States such as United States of America. Moreover, restrictions on the Iranian financial and banking transactions have tightened the pressure on Iran insurance industry as well. The cases brought directly and indirectly before domestic, regional and international courts with regard to Iran insurance industry by the related actors and other economic actors have created a remarkable jurisprudence as to the law of sanctions in general and to the law of insurance in particular.

The question of the present research is exploring the approach of domestic, regional and international courts in the era of sanctions (since 2006 onward) in adjudicating the cases related to the insurance industry. Generally speaking, the judgments of the regional courts such as the EU’s court of justice and domestic courts on Iran’s sanctions, in particular, with respect to insurance industry, indicate to what extent the litigations before the domestic courts could be able to be effective in dealing with the condition to and manner of sanction application and limitations of the UNSC resolutions and the other sanction regulations implementing the UNSC sanctions.

In order to operationally utilize the findings of this research, the resulting policy package in the form of a checklist entitled “Operational Framework for Suing and Pursuing Sanctions-Related Litigation” can be considered as an operational model in future legal solutions in the insurance industry. This framework shows that success in sanctions litigation is based on giving due regard to distinguishing legal levels, focusing on the method of implementation, and activating the fundamental principles of public law and human rights, rather than relying on negating the legality of sanctions own. A successful litigation strategy in the area of sanctions redefines sanctions not as an uncontested political issue, but as an administrative decision that can be controlled through the judicial review.

In the process of any legal action, including filing a lawsuit, the following should be considered: identifying the legal source of the sanction and gaining insight into the framework of the sanction source, distinguishing between an international obligation and the domestic method of implementation, assessing the type of right violated in light of prior relevant judicial practice or arbitration, selecting an appropriate legal framework, purposefully invoking the principle of non-discrimination, activating the principle of proportionality as a central tool of the lawsuit, focusing on formal objections and fair trial elements such as examining compliance with the right to information, hearing, and prior participation. To enter the stage of filing a lawsuit and, in principle, any other legal action in the process of seeking legal action due to the imposition of sanctions and the subsequent damages, it is necessary to take appropriate measures to document the actual effects of the sanctions on the sanctioned person in order to prepare and provide relevant evidence and documents as of standards of the court proceedings. Compliance with the aforementioned criteria significantly increases the chances of winning the lawsuit.

Keywords: sanction, court proceedings, Iran insurance industry, UNSC

Abstract

In the era of digital transformation, fraud detection in financial institutions—particularly within the health insurance sector—has become increasingly critical. This study develops a smart, modular, and scalable framework for health insurance fraud detection using structured claims data, leveraging a hybrid of unsupervised learning algorithms, namely Isolation Forest and K-Means. The framework is designed to identify fraudulent and abusive behaviors independent of actor or service type and to adapt to dynamic and complex environments where fraud patterns evolve over time.

The proposed framework consists of four integrated modules. First, a knowledge-driven module defines the fraud framework and its related features by incorporating insights from insurance and medical experts. This module guides the identification of relevant fraud characteristics and informs feature extraction. Second, a two-stage data warehouse is developed to handle the large volume of claims data and high computational requirements. In the first-stage warehouse, an ETL (Extract–Transform–Load) process ingests claims data, addresses quality issues, removes inconsistencies, and prepares the data for feature extraction. In the second-stage warehouse, relevant features for fraud detection are extracted and selected in collaboration with domain experts. To bridge expert knowledge and algorithmic analysis, a simulation framework allows the medical-insurance team to describe, analyze, and visualize abnormal behaviors, producing a documented list of twenty key features covering actors, products/services, and fraud-specific attributes.

The third module, the fraud detection engine, first partitions data into normal and anomalous clusters using Isolation Forest, and subsequently identifies fraudulent cases with K-Means, forming the hybrid algorithm K-IF. This combination leverages the discriminative power of Isolation Forest and the precise clustering of K-Means, enhancing detection accuracy. The fourth module consists of visualization tools and a managerial dashboard, providing dynamic analysis, interaction, and real-time updates for decision-makers.

Extensive experiments on multiple labeled datasets demonstrate that K-IF outperforms conventional algorithms—including Isolation Forest, LOF, OCSVM, EE, DBSCAN, K-Means, and Autoencoder—in terms of detection accuracy, robustness to contamination rates, edge-case identification, and computational efficiency. Application of the framework to real-world data from a health insurance company confirms its strong anomaly-detection capabilities in practical settings.

Additional contributions include the extraction of expert-validated, scalable features that facilitate not only detection of individual fraud cases but also network-level risk analysis among actors. The proposed framework has been implemented as a software package for private insurance firms, providing advanced analytical tools that significantly improve decision-making processes while minimizing the need for manual intervention. Overall, this study offers a comprehensive, data-driven, and expert-informed solution for fraud detection in health insurance, integrating algorithmic rigor with practical usability and adaptability to complex, evolving fraud scenarios.

Keywords: health insurance fraud; fraud detection; artificial intelligence; unsupervised learning; isolation forest; K-Means; Hybrid Algorithm; feature extraction; simulation framework; expert knowledge; dashboard; insurance analytics; Iran.