Research on Risk Assessment and Underwriting Decision Making Based on ARIMA Model

Abstract

Addressing the critical challenge of extreme weather events, this study introduces a comprehensive model for enhancing insurance underwriting strategies through predictive analytics and risk assessment methodologies. Utilizing an integrated approach that combines Autoregressive Integrated Moving Average (ARIMA) for forecasting extreme weather occurrences, Fuzzy Comprehensive Evaluation for assessing regional payment capabilities, and Monte Carlo simulations for detailed risk quantification, the re-search aims to refine the insurance industry's capacity for anticipating and mitigating financial losses. A systematic clustering analysis further segments regions based on their risk profiles, allowing for tailored insurance premium settings and underwriting decisions. Findings from the application of this model across diverse geographical areas underscore the potential for significantly im-proved risk management and economic resilience in the insurance sector. Through detailed statistical analysis and predictive modeling, the study demonstrates the importance of advanced analytical frameworks in adapting to the evolving dynamics of climate risk.