Prediction of Risk Evaluation Prediction Model for the Insurance Industry under Extreme Weather

Abstract

The insurance industry is one of the indispensable economic sectors of modern society. In recent years, the frequent occurrence and the unpredictable nature of extreme weather have brought economic losses to society reflected in the insurance industry, which prompts people to seek strategies to realize the sustainability of the property insurance industry. This paper addresses the problem by developing a Risk Evaluation Model (REM) for the insurance industry under extreme weather conditions. The model incorporates the Analytic Hierarchy Process (AHP), Entropy Weight Method (EWM), and game theory combination weights to create a comprehensive secondary assessment system. Through fuzzy clustering analysis, risk levels are classified into four categories. The model was validated by applying it to two different regions globally—Florida, USA, and Karachi, Pakistan—determining their insurance risk levels. The results demonstrate that the REM model significantly enhances the accuracy and reliability of risk evaluation, helping to mitigate financial losses and ensure sustainable operations within the insurance industry. The study's significance lies in its contribution to robust risk management and underwriting decisions, with potential applications in urban planning and real estate development, highlighting the model's broader applicability and value.