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Tarification des risques en assurance non-vie, une approche par modèle d’apprentissage statistique

Abstract : Non-life actuarial researches mainly focus on improving Generalized Linear Models. Nevertheless, this type of model sets constraints on the risk structure and on the interactions between explanatory variables. Then, a bias between the real risk and the predicted risk by the model is often observed on a part of data. Nonparametric tools such as machine learning algorithms are more efficient to explain the singularity of the policyholder. Among these models, regression trees offer the benefit of both reducing the bias and improving the readability of the results of the pricing estimation. Our study introduces a modification of the Classification And Regression Tree (CART) algorithm to take into account the specificities of insurance data-sets. It compares the results produced by this algorithm to these obtained using Generalized Linear Models. These two approaches are then applied to the pricing of a vehicle insurance portfolio.
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https://hal.archives-ouvertes.fr/hal-02151827
Contributor : Martial Phélippé-Guinvarc'H <>
Submitted on : Thursday, June 13, 2019 - 10:00:54 AM
Last modification on : Thursday, November 14, 2019 - 9:40:05 AM

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  • HAL Id : hal-02151827, version 1

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Antoine Pagliat, Martial Phélippé-Guinvarc'H. Tarification des risques en assurance non-vie, une approche par modèle d’apprentissage statistique. Bulletin français d'actuariat, 2011, 11, pp.49 - 81. ⟨hal-02151827⟩

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