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Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Author

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  • Christopher Blier-Wong

    (École d’actuariat, Université Laval, Québec, QC G1V 0A6, Canada)

  • Hélène Cossette

    (École d’actuariat, Université Laval, Québec, QC G1V 0A6, Canada)

  • Luc Lamontagne

    (Département d’informatique et de génie logiciel, Université Laval, Québec, QC G1V 0A6, Canada)

  • Etienne Marceau

    (École d’actuariat, Université Laval, Québec, QC G1V 0A6, Canada)

Abstract

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

Suggested Citation

  • Christopher Blier-Wong & Hélène Cossette & Luc Lamontagne & Etienne Marceau, 2020. "Machine Learning in P&C Insurance: A Review for Pricing and Reserving," Risks, MDPI, vol. 9(1), pages 1-26, December.
    • Handle: RePEc:gam:jrisks:v:9:y:2020:i:1:p:4-:d:467315
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    References listed on IDEAS

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    Cited by:

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    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. Tanut Treetanthiploet & Yufei Zhang & Lukasz Szpruch & Isaac Bowers-Barnard & Henrietta Ridley & James Hickey & Chris Pearce, 2023. "Insurance pricing on price comparison websites via reinforcement learning," Papers 2308.06935, arXiv.org.
    4. Shengkun Xie & Rebecca Luo, 2022. "Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions," Mathematics, MDPI, vol. 10(10), pages 1-19, May.
    5. Shengkun Xie & Kun Shi, 2023. "Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective," Mathematics, MDPI, vol. 11(2), pages 1-24, January.
    6. Catalina Lozano-Murcia & Francisco P. Romero & Jesus Serrano-Guerrero & Jose A. Olivas, 2023. "A Comparison between Explainable Machine Learning Methods for Classification and Regression Problems in the Actuarial Context," Mathematics, MDPI, vol. 11(14), pages 1-20, July.

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