Improved adaptive genetic algorithm for the vehicle insurance fraud identification model based on a BP neural network

With the development of the insurance industry, insurance fraud is increasing rapidly. The existence of insurance fraud considerably hinders the development of the insurance industry. Fraud identification has become the most important part of insurance fraud research. In this paper, an improved adaptive genetic algorithm (NAGA) combined with a BP neural network (BP neural network) is proposed to optimize the initial weight of BP neural networks to overcome their shortcomings, such as ease of falling into local minima, slow convergence rates and sample dependence. Finally, the historical automobile insurance claim data of an insurance company are taken as a sample. The NAGA-BP neural network model was used for simulation and prediction. The empirical results show that the improved genetic algorithm is more advanced than the traditional genetic algorithm in terms of convergence speed and prediction accuracy.