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23 August 2022 Research on auto insurance pricing based on LightGBM
Lin Zhang, JiangPing Yang
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Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123300D (2022) https://doi.org/10.1117/12.2646305
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China
Abstract
At present, China's auto insurance pricing is relatively rough, without considering the vehicle type factor, and the use of a generalized linear model has many limitations, resulting in the low accuracy of auto insurance rating. In order to solve the above problems, this paper integrates the vehicle physical collision test results and other vehicle factors into the automobile insurance pricing model. Based on the vehicle physical parameters and vehicle physical collision test data, claims data, and underwriting data, this paper establishes the GLM and the cutting-edge machine learning model LightGBM respectively and compares the automobile insurance pricing methods. The findings reveal that the new model component incorporated into the pricing model has a significant impact on the final vehicle damage insurance total claim. In addition, the prediction accuracy and model robustness of the machine learning model LightGBM in dealing with the entire amount of car damage insurance claims with numerous features and huge data are higher than those of the classic vehicle insurance pricing model GLM. This paper lays out a workable plan for achieving a fine vehicle insurance pricing that is both fair and unique.
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Lin Zhang and JiangPing Yang "Research on auto insurance pricing based on LightGBM", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123300D (23 August 2022); https://doi.org/10.1117/12.2646305
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KEYWORDS
Data modeling
Machine learning
Statistical analysis
Safety
Parallel computing
Performance modeling
Statistical modeling
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