Univrsity Of Tehran Press Financial Research Journal 1024-8153 27 4 2025 11 22 A Financial Evaluation Model for Insurance Companies’ Management of Claimed Loss Risks under Normal and Crisis Conditions A Financial Evaluation Model for Insurance Companies’ Management of Claimed Loss Risks under Normal and Crisis Conditions 1036 1058 105807 10.22059/frj.2026.404825.1007807 FA Maryam Esna-Ashari Assistant Prof., Department of Property and Casualty Insurance, Iranian Insurance Research Center, Tehran, Iran. 0000-0001-5337-9665 Moslem Nilchi Assistant Prof., Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran. 0000-0001-8946-4693 Farzan Khamesian Assistant Prof., Department of General Insurance, Iranian Insurance Research Center, Tehran, Iran. 0000-0001-6113-4246 Journal Article 2025 10 22 Objective While insurance companies operate with profit-maximization objectives similar to those of other economic sectors, they possess distinct structural characteristics in financial intermediation. Owing to their role in risk transfer and the resulting concentration of risk, their operational sustainability is uniquely vulnerable. Traditional financial solvency ratios, although effective in assessing capital adequacy, do not necessarily ensure short-term liquidity resilience during catastrophic, fat-tailed loss events. Accordingly, this study aims to develop a model for assessing the resilience of insurance companies in managing claims risk under both normal and critical scenarios, without reliance on restrictive prior assumptions regarding loss distributions.       Methods This study employs an applied, descriptive-analytical research design, focusing on the analysis of historical data from the insurance sector. The statistical population comprises all general insurance companies listed on the Tehran Stock Exchange (TSE) over the period from 2014 to 2024. Sample selection was contingent upon data accessibility and the availability of quarterly financial statements. The dataset incorporates quarterly financial reports and internal corporate records, specifically the total number of issued policies, total written and collected premiums, and the aggregate frequency and severity of paid claims on a quarterly basis. To mitigate inflationary distortions, monetary variables—specifically claim amounts and asset values—were adjusted for inflation using the Consumer Price Index (CPI). Subsequently, a logarithmic differencing filter was applied to detrend the data and ensure stationarity. In the final phase, optimal statistical distributions were fitted to the empirical data. The financial health of insurers was then evaluated by defining and calculating three novel metrics: the Risk Status Index, the Risk Coverage Ratio (Normal Condition), and the Risk Coverage Ratio (Critical Condition).   Results The results demonstrate that the proposed indicators exhibit significantly greater explanatory power than conventional regulatory solvency ratios. The findings indicate that even entities maintaining a standard regulatory solvency ratio (Level 1) may experience “hidden insolvency” as a result of inefficiencies in managing asset quality and liquidity.   Conclusion The proposed model establishes that liquidity risk management must be integrated as an essential complement to traditional solvency frameworks. Implementation of this model enables supervisory authorities to identify early warning signals prior to the onset of irreversible financial distress. Objective While insurance companies operate with profit-maximization objectives similar to those of other economic sectors, they possess distinct structural characteristics in financial intermediation. Owing to their role in risk transfer and the resulting concentration of risk, their operational sustainability is uniquely vulnerable. Traditional financial solvency ratios, although effective in assessing capital adequacy, do not necessarily ensure short-term liquidity resilience during catastrophic, fat-tailed loss events. Accordingly, this study aims to develop a model for assessing the resilience of insurance companies in managing claims risk under both normal and critical scenarios, without reliance on restrictive prior assumptions regarding loss distributions.       Methods This study employs an applied, descriptive-analytical research design, focusing on the analysis of historical data from the insurance sector. The statistical population comprises all general insurance companies listed on the Tehran Stock Exchange (TSE) over the period from 2014 to 2024. Sample selection was contingent upon data accessibility and the availability of quarterly financial statements. The dataset incorporates quarterly financial reports and internal corporate records, specifically the total number of issued policies, total written and collected premiums, and the aggregate frequency and severity of paid claims on a quarterly basis. To mitigate inflationary distortions, monetary variables—specifically claim amounts and asset values—were adjusted for inflation using the Consumer Price Index (CPI). Subsequently, a logarithmic differencing filter was applied to detrend the data and ensure stationarity. In the final phase, optimal statistical distributions were fitted to the empirical data. The financial health of insurers was then evaluated by defining and calculating three novel metrics: the Risk Status Index, the Risk Coverage Ratio (Normal Condition), and the Risk Coverage Ratio (Critical Condition).   Results The results demonstrate that the proposed indicators exhibit significantly greater explanatory power than conventional regulatory solvency ratios. The findings indicate that even entities maintaining a standard regulatory solvency ratio (Level 1) may experience “hidden insolvency” as a result of inefficiencies in managing asset quality and liquidity.   Conclusion The proposed model establishes that liquidity risk management must be integrated as an essential complement to traditional solvency frameworks. Implementation of this model enables supervisory authorities to identify early warning signals prior to the onset of irreversible financial distress. https://jfr.ut.ac.ir/article_105807_6c55865dfc63c695c15013454e5b3ddc.pdf