Strategies for Mitigating the Impact of Inflation on Life Insurance in the Iranian Insurance Industry

Author(s): Dr. Mitra Ghanbarzadeh

Coordinator(s): Dr. Asma Hamzeh, Dr. Nasrin Hozzar Moghadam, Zahra Majedi, Dr. Zahra Barzegar, Mohammad Sattari, Dr. Said Sehhat

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

Developing a Roadmap for the Implementation of Intelligent Supervision in Iran’s Insurance Industry

Author(s): Dr. Ameneh Khadivar

Coordinator(s): Dr. Leili Niakan, Dr. Ali Rahmani, Raha Basraei, Dr. Marzieh Sharoudi, Abdollah Astin, Hediyeh Ali, Dr. Mehdi Khani

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

Analysis of the Domestic and International Insurance Related Court Proceedings from the International Law Perspective with a Special Focus on Iran Insurance Industry Sanctions

Author(s): Dr. Abdollah Abedini

Coordinator(s): Dr. Fatemeh Azadbakht

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

Detection of Health Insurance Fraud through the Application of Artificial Intelligence Algorithms

Authors: Dr. Mojtaba Farokh

Coordinator: Sirous Sharifi, Dr. Nasrin Hezar-Moghadam, Dr. Abbas Rad, Dr. Mehdi Riyahi Far, Alireza Norouzi, Alireza Emami Fard, Zahra Teymoori

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.