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| 題名: | 可解釋機器學習之預測: 以法國第三人責任險成功索賠為例
Interpretable Machine Learning for Prediction: Numbers of Successful Claims in French Third-Party Liability Insurance |
| 作者: | 汪于崴
Wang, Yu-Wei |
| 貢獻者: | 洪芷漪
林士貴
汪于崴
Wang, Yu-Wei |
| 關鍵詞: | 法國第三人責任險
成功索賠次數
廣義線性模型
可解釋機器學習
French third-party liability insurance
Number of successful claims
Generalized linear models
Interpretable machine learning |
| 日期: | 2025 |
| 上傳時間: | 2025-09-01 16:30:33 (UTC+8) |
| 摘要: | 車禍事故的外部性常導致無辜第三方蒙受損失，第三人責任險因此成為重要的風險分擔工具。本研究旨在利用可解釋機器學習方法，預測法國第三人責任險的成功索賠次數，並提升模型的解釋性與預測精度。研究採用公開的法國第三人責任險資料集，基於Zero-Inflated Poisson (ZIP)和Zero-Inflated Negative Binomial (ZINB)兩分佈，結合Boosted Trees和DART建構預測模型。透過特徵重要性分析與累積局部效應（ALE），本研究揭示影響索賠頻率的關鍵因素。結果顯示，Boosted Trees和DART模型在損失函數、Pseudo R2 和Gini2 等評估指標上均優於傳統廣義線性模型(GLM)，且具備更高的可解釋性。本研究不僅驗證可解釋機器學習在保險精算中的應用潛力，還為第三人責任險的定價與風險管理提供實證依據，未來可進一步拓展至其他保險市場。
The externality of car accidents often imposes losses on innocent third parties, making third-party liability insurance a crucial risk-sharing mechanism. This study aims to predict the number of successful claims in French third-party liability insurance using interpretable machine learning methods, while enhancing both model interpretability and prediction accuracy. Utilizing a publicly available French third-party liability insurance dataset, we construct predictive models based on Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) distributions, integrated with Boosted Trees and DART techniques. Through feature importance analysis and Accumulated Local Effects (ALE), this study identifies key factors influencing claim frequency. Results demonstrate that the Boosted Trees and DART models outperform traditional Generalized Linear Models (GLMs) across evaluation metrics such as the loss function, Pseudo R², and Gini coefficient, while offering greater interpretability. This study not only validates the potential of interpretable machine learning in actuarial science but also provides empirical insights for pricing and risk management in third-party liability insurance, with potential applications in other insurance markets. |
| 參考文獻: | Aeron-Thomas, A. (2002). The role of the motor insurance industry in preventing and compensating road casualties: scoping study final report.
Antipov, E. A. and Pokryshevskaya, E. B. (2020). Interpretable machine learning for demand modeling with high-dimensional data using gradient boosting machines and shapley values.19(5).
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. CoRR, abs/1603.02754.
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1):1–38.
Dutang, C., Charpentier, A., and Gallic, E. (2024). Insurance dataset.
Gavriletea, M. D. and Moga, A. C. (2011). Romanian compulsory motor third party liability insurance in 2010 and the predictable future. Women.
Hsieh, S.-H., Liu, C.-T., and Tzeng, L. Y. (2014). Insurance marketing channel as a screening mechanism: Empirical evidences from taiwan automobile insurance market. The Geneva Papers on Risk and Insurance. Issues and Practice, 39(1):90–103.
Liu, C.-T., Chang, C.-H., and Chen, H. H. (2024). Underwriting information and insurers’profitability: Evidence from automobile physical damage insurance in taiwan. Pacific-Basin Finance Journal, 83:102267.
Meng, S., Gao, Y., and Huang, Y. (2022). Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees. Insurance: Mathematics and Economics, 106:115–127.
Qazvini, M. (2019). On the validation of claims with excess zeros in liability insurance: A comparative study. Risks, 7(3).
Rashmi, K. V. and Gilad-Bachrach, R. (2015). DART: dropouts meet multiple additive regression trees. CoRR, abs/1505.01866.
Tiruneh, A. T. (2013). Higher order aitken extrapolation with application to converging and diverging gauss-seidel iterations.
Venezia, I., Galai, D., and Shapira, Z. (1999). Exclusive vs. independent agents: a separating equilibrium approach. Journal of Economic Behavior Organization, 40(4):443–456.
Wibisono, A., Wilson, A. C., and Jordan, M. I. (2016). A variational perspective on accelerated methods in optimization. Proceedings of the National Academy of Sciences, 113(47).
Wüthrich, M. V. and Merz, M. (2023). Appendix B: Data and Examples, pages 553–575. Springer International Publishing, Cham.
Ye, C., Zhang, L., Han, M., Yu, Y., Zhao, B., and Yang, Y. (2022). Combining predictions of auto insurance claims.
Zhao, X., Zhang, L., Zhu, G., Cheng, C., He, J., Traore, S., and Singh, V. P. (2023). Exploring interpretable and non-interpretable machine learning models for estimating winter wheat evapotranspiration using particle swarm optimization with limited climatic data. Computers and Electronics in Agriculture, 212:108140.
Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 67(2):301–320. |
| 描述: | 碩士
國立政治大學
應用數學系
111751010 |
| 資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0111751010 |
| 資料類型: | thesis |
| 顯示於類別: | [應用數學系] 學位論文
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