Estimating Ruin Probability in an Insurance Risk Model Using the Wang-PH Transform Through Claim Simulation

Abstract

The accurate estimation of ruin probability is a fundamental challenge in non-life insurance, impacting financial stability, risk management strategies, and operational decisions. This study aims to propose an approach for estimating ruin probability using claim simulation enhanced by the Wang-PH transform to fit various loss distributions, including Gamma, Weibull, Lognormal, Log-logistic, Inverse Weibull, and Inverse Gaussian, to actual claim data. Methods involve the transformation of loss distributions via the Wang-PH transform and rigorous evaluation to select the optimal distribution model that best reflects actual claim characteristics. This model serves as the foundation for estimating finite-time ruin probability through claim simulation, employing the acceptance-rejection technique to generate random samples. Additionally, a regression-based methodology estimates the minimum capital reserve required to safeguard against financial risk. Findings indicate the proposed method's computational efficiency, making it a valuable tool for insurers and risk analysts in assessing and mitigating financial risks in the non-life insurance sector. The novelty of this study lies in the integration of the Wang-PH transform with empirical data fitting and simulation techniques, applied to estimating ruin probability and determining capital reserves. Doi: 10.28991/ESJ-2025-09-01-011 Full Text: PDF

Keywords

• claim simulation
• loss distribution
• minimum capital reserve
• non-life insurance
• ruin probability
• wang transform.