Research on UBI Auto Insurance Pricing Model Based on Adaptive SAPSO to Optimize the Fuzzy Controller

Abstract

In order to accurately determine the auto insurance rate of UBI, this paper proposes to use fuzzy controller to calculate the rate and optimize it by using the simulated annealing particle swarm algorithm with Metropolis criterion. Firstly, a fuzzy controller is constructed by selecting monthly mileage and violation times to calculate the self-underwriting coefficient. In order to eliminate the subjectivity defect of fuzzy controller, the correlation function of independent underwriting coefficient and historical risk data is proposed as the fitness function of evaluating fuzzy rules, using adaptive simulated annealing particle swarm optimization algorithm is intelligent search, according to the fitness value of continual iteration and optimize the optimal fuzzy rules. Finally, the fuzzy controller is reconstructed with the optimal fuzzy rules to estimate the auto insurance rate accurately. The results show that the adaptive simulated annealing particle swarm optimization algorithm can effectively extract the driving behavior information and can calculate the more reasonable and accurate autonomous underwriting coefficient. The results are highly correlated with the number of historical accidents and have the ability and stability of risk quantification.