A Study of Insurance Company Underwriting Strategies Based on TOPSIS Entropy Weights and Cost-Benefit Analysis

Abstract

Frequent extreme weather events triggered by climate change pose an increasing risk to property owners and insurance companies. To address this challenge, this paper explores how to optimize resource allocation strategies for property insurance with the aim of improving the ability of the insurance system to pay future claims while safeguarding the long-term stability of the company. In order to give an underwriting strategy for disaster-prone areas, the number of natural disasters, the number of people affected, and the economic losses are screened as key influencing factors. Subsequently, TOPSIS entropy weighting analysis was used to assign appropriate weights to these factors, and the K-mean algorithm was used to cluster these data into low, medium, and high-risk levels. To more accurately assess underwriting risk, I constructed an underwriting risk assessment model using cost-benefit analysis. Applied to the U.S. and Switzerland, the scores were 0.49 and 0.02, respectively. This suggests that the U.S. is a high-risk country and cannot be insured, while Switzerland is in a low-risk area and can be insured.