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This study aims to improve the efficiency of insurance underwriting decisions and assess community risks, and constructs a dynamic premium pricing model through the ARIMA model. In-depth analysis of historical data provides quantitative and optimal support for insurers to address premium setting and underwriting decision challenges. The results show that the model can predict future risks and losses, facilitate risk classification and premium determination. The classified statistical method analyzes the frequency and time correlation of extreme meteorological events, and provides basis for insurance policy adjustment. Research provides more accurate and dynamic premium pricing and underwriting decision-making tools, simplifies the risk assessment process, and is conducive to insurance market stability and resource optimization, service optimization and long-term market stability.
Wu et al. (Thu,) studied this question.