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Machine Learning in Ratemaking, an Application in Commercial Auto Insurance

Author

Listed:
  • Spencer Matthews

    (Department of Statistics, Donald Bren School of Information and Computer Science, University of California—Irvine, Irvine, CA 92697, USA)

  • Brian Hartman

    (Department of Statistics, College of Physical and Mathematical Sciences, Brigham Young University, Provo, UT 84602, USA)

Abstract

This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These datasets include bodily injury (BI), property damage (PD) and collision (COLL) coverage, each documenting policy characteristics and claims across a four-year period. The datasets are explored, including summaries of all variables, then the methods for modeling are set forth. Models are tuned and the tuning results are displayed, after which we train the final models and seek to explain select predictions. Data were provided by a private insurance carrier to the CAS after anonymizing the dataset. These data are available to actuarial researchers for well-defined research projects that have universal benefit to the insurance industry and the public. Our hope is that the methods demonstrated here can be a good foundation for future ratemaking models to be developed and tested more efficiently.

Suggested Citation

  • Spencer Matthews & Brian Hartman, 2022. "Machine Learning in Ratemaking, an Application in Commercial Auto Insurance," Risks, MDPI, vol. 10(4), pages 1-25, April.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:4:p:80-:d:789292
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    References listed on IDEAS

    as
    1. Spencer Matthews & Brian Hartman, 2021. "mSHAP: SHAP Values for Two-Part Models," Risks, MDPI, vol. 10(1), pages 1-23, December.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    Full references (including those not matched with items on IDEAS)

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