长春工程学院学报（自然科学版）2024，Vol.25Issue(1)：78-84,7.DOI:10.3969/j.issn.1009-8984.2024.01.015
基于GA-BP神经网络的车险索赔频率预测与优化研究
Research on Prediction and Optimization of Vehicle Insurance Claim Frequency Based on GA-BP Neural Network
摘要
Abstract
The prediction of the frequency of car insurance claims is of great significance for car insurance pricing.In recent years,with the rise of big data technology,traditional car insurance pricing models can no longer meet the increasing demand for a large amount of customer data from insurance companies.In order to improve the prediction accuracy of car insurance claim frequency,a real data of car insurance customers from a French insurance company is used,and a genetic algorithm is added to the BP neural network to compare the relevant models and select the optimal model.The research results indicate that the prediction accuracy of the genetic algorithm optimization model is significantly better than that of the BP neural net-work,and its performance in predicting the frequency of car insurance claims is better,which can effectively reduce the pricing cost of car insurance.关键词
汽车保险/索赔频率/遗传算法/BP神经网络/ROC曲线Key words
auto insurance/claim frequency/genetic algorithm/BP neural network/ROC curve分类
信息技术与安全科学引用本文复制引用
肖阳田,肖鸿民..基于GA-BP神经网络的车险索赔频率预测与优化研究[J].长春工程学院学报（自然科学版）,2024,25(1):78-84,7.基金项目
国家自然科学基金项目(12061066)甘肃省自然科学基金(20JR5RA528) （12061066）
