关键词: 保险, 暴雨, 理赔数量模拟模型, 最大小时雨强, 风险阈值, 石家庄

Abstract:

 In order to cope with climate change, adapt to urban development and reduce the risk of urban meteorological disasters, based on the insurance claim data caused by meteorological disaster and surface meteorological observation data of four districts in Shijiazhuang city from 2008 to 2018, statistical methods were used to analyze the change characteristics and disaster-causing factor of urban insurance claim quantity caused by heavy rain. The simulated model for claim quantity due to heavy rain was constructed by considering the disaster-causing factor as an independent variable and the claim quantity as a dependent variable. The feasibility of the model was tested by using F−test, ANOVA and actual cases from 2019 to 2021. Finally, risk level thresholds of insurance claim caused by heavy rain were divided based on the key meteorological factor. The results were as follows: heavy rain was the main disaster that led to weather-related insurance claims of four districts in Shijiazhuang city, and the claim quantity due to heavy rain showed a significant increase trend. Five simulated models of claims quantity due to heavy rain were developed using either one or multiple linear regression analysis methods. After testing, it was determined that the maximum rainfall intensity was the key factor affecting the claim quantity of four districts in Shijiazhuang city. Using the maximum rainfall intensity as risk index, the risk of disaster claims due to heavy rain was divided into four levels: mild, moderate, severe and extremely severe. On this basis, through the development of heavy rain risk monitoring system for urban insurance industry in the later stage, it can essentially meet the risk monitoring and early warning needs of the industry, and provide technical support for promoting the risk reduction service of "prevention is more important than compensation" in the insurance industry. 

Key words: Insurance, Heavy rain, Claim quantity simulation model, Maximum rainfall intensity, Risk threshold, Shijiazhuang