Regional Disaster Vulnerability Analysis and Insurance Selection Analysis Based on AHP-EWM-TOPSIS Group Method

Le Zhang; Shuai Lin; Peimin Gu

doi:10.54097/63ment96

Authors

Le Zhang
Shuai Lin
Peimin Gu

DOI:

https://doi.org/10.54097/63ment96

Keywords:

BP Neural Network, TOPSIS, AHP, EWM.

Abstract

Floods, hurricanes, cyclones, and droughts are the most common types of weather, and they have a profound impact on insurance. To specifically analyse the impact, the paper first introduces the concept of Regional Resilience Index (RRI), and then use the AHP-EWM-TOPSIS Method to derive the RRI scores and categorize all regions into five categories based on the size of RRI, which are rock-solid, stable, moderate, weak and vulnerable. Next, the Insurance Break-even Index Evaluation Model is built through the analysis. Insurers should increase their coverage in rock-solid, stable and moderate areas in line with the equilibrium curve, where they should invest in extensive publicity. In weak and vulnerable areas, insurance companies should appropriately reduce policy investment, build a multi-level insurance portfolio such as reinsurance mechanism, and invest a certain amount of funds in regional disaster prevention and loss prevention to maximize the win-win situation between the insurance company and the insured.

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