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Wasserstein boosting trees algorithm for count data, with application to claim frequencies in motor insurance

Author

Listed:
  • Denuit, Michel

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Michaelides, Marie

    (Heriot-Watt University)

  • Trufin, Julien

    (ULB)

  • Verelst, Harrison

    (Detralytics)

Abstract

This paper proposes a variant of the well-known boosting trees algorithm to estimate conditional distributions. Since regression trees partition observations into subgroups, the corresponding empirical distributions can be used to define the splitting criterion. Precisely, the parametric approach using Poisson deviance is replaced with a non-parametric one maximizing probabilistic distances between empirical distributions in child nodes. Proceeding inthis way, the actuary obtains an estimated conditional distribution for the response, from which a conditional mean can be derived as well as any other quantity of interest in risk management. The numerical performances of the proposed method are assessed with simulated data while a case study demonstrates its usefulness for insurance applications.

Suggested Citation

  • Denuit, Michel & Michaelides, Marie & Trufin, Julien & Verelst, Harrison, 2025. "Wasserstein boosting trees algorithm for count data, with application to claim frequencies in motor insurance," LIDAM Discussion Papers ISBA 2025024, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2025024
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    References listed on IDEAS

    as
    1. Delong, Łukasz & Kozak, Anna, 2023. "The use of autoencoders for training neural networks with mixed categorical and numerical features," ASTIN Bulletin, Cambridge University Press, vol. 53(2), pages 213-232, May.
    2. Denuit, Michel & Hainaut, Donatien & Trufin, Julien, 2020. "Effective Statistical Learning Methods for Actuaries II : Tree-Based Methods and Extensions," LIDAM Reprints ISBA 2020035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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