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Improved adaptive genetic algorithm for the vehicle Insurance Fraud Identification Model based on a BP Neural Network

机译：基于BP神经网络的车辆保险欺诈识别模型改进了改进的自适应遗传算法

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摘要

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. (C) 2019 Elsevier B.V. All rights reserved.
机译：随着保险业的发展，保险欺诈正在迅速增加。 保险欺诈的存在大大阻碍了保险业的发展。 欺诈识别已成为保险欺诈研究中最重要的部分。 在本文中，提出了一种改进的自适应遗传算法（NAGA）与BP神经网络（BP神经网络）相结合，以优化BP神经网络的初始重量以克服其缺点，例如易于陷入局部最小值，缓慢收敛 速率和样本依赖。 最后，保险公司的历史汽车保险索赔数据被视为样本。 Naga-BP神经网络模型用于模拟和预测。 经验结果表明，在收敛速度和预测精度方面，改进的遗传算法比传统遗传算法更进一步。 （c）2019 Elsevier B.v.保留所有权利。

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