Making The Best Investment Strategy for Insurance Companies Under The Background of Extreme Weather

Abstract

In the face of the negative impacts of the frequent occurrence of extreme weather disasters around the world, we have developed natural disaster risk prediction and analysis models for insurance companies to maximize their benefits. This paper first selects six risk indicators, such as flood, cold wave, drought and other disasters, and combines the analytic hierarchy process (AHP) and grey prediction model (GM) to calculate the impact weights of six kinds of natural disasters, and predicts the probability of future occurrence, so as to realize the comprehensive assessment of natural disaster risk. This paper also analyzes the effect of the owner's intention to buy insurance on the insurance strategy through the function model, and solves the optimal insurance strategy through the technique for order preferenceby similarity to ideal solution (TOPSIS) and neural network. We define the ratio of the total profit of natural disaster insurance to the total cost as the decision coefficient K. When K is greater than 35%, the insurance company chooses to write the policy, and when K is greater than 5% and less than 35%, the insurance company chooses to bear the risk. Finally, this paper collected the data of Florida and Kyushu, Japan. Through the insurance model, it calculated that the total profit of natural disaster insurance in Florida was 635.8 million US dollars, while that in Kyushu was -61.51 million US dollars. Therefore, insurance companies should invest in Florida.