Auto insurance fraud detection based on Logistic-SVM algorithm

Hongyu Lv; Xinyan Liu; Shancheng Lin; Xiao Ruan; Ning Ding

doi:10.1117/12.2634651

4 May 2022 Auto insurance fraud detection based on Logistic-SVM algorithm

Hongyu Lv, Xinyan Liu, Shancheng Lin, Xiao Ruan, Ning Ding

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 121720Y (2022) https://doi.org/10.1117/12.2634651
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

In recent years, with the continuous expansion of the business scope of auto insurance, the crime of auto insurance fraud is becoming frequent. The establishment of auto insurance fraud detection model has become an important measure to ensure the stable development of auto insurance industry. This paper builds an auto insurance fraud detection model based on Logistic-SVM, which could solve the problem that the original model needs lots of variables. Firstly, the importance of characteristic variables is sorted by SVM model, and ten characteristic variables are selected according to the objective reality. By comparing with the traditional single logistic regression and SVM algorithm, it is found that the Logistic-SVM algorithm has a better detection effect on auto insurance fraud. The accuracy of Logistic-SVM is 96.1%, which is 2% higher than that of logistic-regression and 0.7% higher than that of SVM. The research of this paper could not only improve the practicability of machine learning model in the field of auto insurance fraud detection, but also escort the prosperity and development of auto insurance industry.

Citation Download Citation

Hongyu Lv, Xinyan Liu, Shancheng Lin, Xiao Ruan, and Ning Ding "Auto insurance fraud detection based on Logistic-SVM algorithm", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 121720Y (4 May 2022); https://doi.org/10.1117/12.2634651

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