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Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees

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  • Meng, Shengwang
  • Gao, Yaqian
  • Huang, Yifan

Abstract

Usage-based insurance (UBI) is now a sought-after auto insurance product in the market. By using a wide range of telematics data, insurance companies can better understand the insured's driving behavior and capture the relationship between insurance loss and the relevant risk factors. This study examines the frequency of UBI claims and combines machine learning algorithms with classic actuarial distributions to establish the predictive model. More specifically, considering the large number of driving behavior features and their complex interactions, we replace generalized linear models with boosted trees, and synchronously update the estimation results of the zero-inflation probability and mean parameter under a zero-inflated Poisson or zero-inflated negative binomial assumption. We further discuss the role of regularization terms and “dropout” in dual-parameter boosted trees, and propose a general framework for insurance claim frequency modeling, which shows high prediction accuracy on both UBI and French motor third-party liability datasets, as well as the interpretability. The potential of extensive driving behavior features has been further verified on a Chinese insurance dataset, and the factors that have a significant impact on vehicle risk are identified and quantified on this basis. In addition, we discuss in detail the key points of applying boosted trees in actuarial science, which also promotes predictive insurance analytics.

Suggested Citation

  • Meng, Shengwang & Gao, Yaqian & Huang, Yifan, 2022. "Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 115-127.
    • Handle: RePEc:eee:insuma:v:106:y:2022:i:c:p:115-127
    DOI: 10.1016/j.insmatheco.2022.06.001
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Yaojun & Ji, Lanpeng & Aivaliotis, Georgios & Taylor, Charles, 2024. "Bayesian CART models for insurance claims frequency," Insurance: Mathematics and Economics, Elsevier, vol. 114(C), pages 108-131.
    2. Yaojun Zhang & Lanpeng Ji & Georgios Aivaliotis & Charles Taylor, 2023. "Bayesian CART models for insurance claims frequency," Papers 2303.01923, arXiv.org, revised Dec 2023.
    3. Yang Qiao & Chou-Wen Wang & Wenjun Zhu, 2024. "Machine learning in long-term mortality forecasting," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 340-362, April.
    4. Carina Clemente & Gracinda R. Guerreiro & Jorge M. Bravo, 2023. "Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting," Risks, MDPI, vol. 11(9), pages 1-20, September.

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    More about this item

    Keywords

    Usage-based insurance; Driving behavior features; Boosted trees; Zero-inflated distribution; Predictive insurance analytics;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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