Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network
The BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amoun...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6616121 |
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| author | Wenguang Yu Guofeng Guan Jingchao Li Qi Wang Xiaohan Xie Yu Zhang Yujuan Huang Xinliang Yu Chaoran Cui |
| author_facet | Wenguang Yu Guofeng Guan Jingchao Li Qi Wang Xiaohan Xie Yu Zhang Yujuan Huang Xinliang Yu Chaoran Cui |
| author_sort | Wenguang Yu |
| collection | DOAJ |
| description | The BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amount in automobile insurance is the generalized linear model, where the BP neural network model could provide a different approach to estimate the total claim loss. This paper uses a genetic algorithm to optimize the structure of the BP neural network at first, and the calculation speed is significantly improved. At the same time, by considering the overfitting problem, an early stop method is introduced to avoid the overfitting problem. In the model, a three-layer BP neural network model, which includes the input layer, hidden layer, and output layer, is trained. With consideration of various factors, a total claim amount prediction model is established, and the trained BP neural network model is used to predict the total claim amount of automobile insurance based on the data of the training set. The results show that the accuracy of the prediction by using the BP neural network model to both the data of Shandong Province and to the data of six cities is over 95%. Then, the predicted total claim amount is used to calculate premiums for five cities in Shandong Province according to credibility theory. The results show that the average premium of the five cities is slightly higher than the actual claim amount of the city. The combination of BP neural network and credibility theory can perform accurate claim amount estimation and pricing for automobile insurance, which can effectively improve the current situation of the automobile insurance business and promote the development of insurance industry. |
| format | Article |
| id | doaj-art-23acc586401740d787e03ea9a2c586f9 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-23acc586401740d787e03ea9a2c586f92025-02-03T01:04:05ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66161216616121Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural NetworkWenguang Yu0Guofeng Guan1Jingchao Li2Qi Wang3Xiaohan Xie4Yu Zhang5Yujuan Huang6Xinliang Yu7Chaoran Cui8School of Insurance, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Mathematic and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250014, ChinaCollege of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, ChinaSchool of Mathematic and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Insurance, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Insurance, Shandong University of Finance and Economics, Jinan 250014, ChinaOffice of Academic Research, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Insurance, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Computer Science & Technology, Shandong University of Finance and Economics, Jinan 250014, ChinaThe BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amount in automobile insurance is the generalized linear model, where the BP neural network model could provide a different approach to estimate the total claim loss. This paper uses a genetic algorithm to optimize the structure of the BP neural network at first, and the calculation speed is significantly improved. At the same time, by considering the overfitting problem, an early stop method is introduced to avoid the overfitting problem. In the model, a three-layer BP neural network model, which includes the input layer, hidden layer, and output layer, is trained. With consideration of various factors, a total claim amount prediction model is established, and the trained BP neural network model is used to predict the total claim amount of automobile insurance based on the data of the training set. The results show that the accuracy of the prediction by using the BP neural network model to both the data of Shandong Province and to the data of six cities is over 95%. Then, the predicted total claim amount is used to calculate premiums for five cities in Shandong Province according to credibility theory. The results show that the average premium of the five cities is slightly higher than the actual claim amount of the city. The combination of BP neural network and credibility theory can perform accurate claim amount estimation and pricing for automobile insurance, which can effectively improve the current situation of the automobile insurance business and promote the development of insurance industry.http://dx.doi.org/10.1155/2021/6616121 |
| spellingShingle | Wenguang Yu Guofeng Guan Jingchao Li Qi Wang Xiaohan Xie Yu Zhang Yujuan Huang Xinliang Yu Chaoran Cui Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network Complexity |
| title | Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network |
| title_full | Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network |
| title_fullStr | Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network |
| title_full_unstemmed | Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network |
| title_short | Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network |
| title_sort | claim amount forecasting and pricing of automobile insurance based on the bp neural network |
| url | http://dx.doi.org/10.1155/2021/6616121 |
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