Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data

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      <subfield code="a">Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data</subfield>
      <subfield code="c">Arthur Maillart</subfield>
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      <subfield code="a">In this paper, we suggest an explainable machine learning approach to model the claim frequency of a telematics car dataset. In fact, we use a data-driven method based on tree ensembles, namely, the random forest, to create a claim frequency model. Then, we present a method to build a tree that faithfully synthesizes the predictions of a tree ensemble model such as those derived from the random forest or gradient boosting. This tree serves as a global explanation of the predictions of the black-box. Thanks to this surrogate model, we can extract knowledge from a black-box tree ensemble model. Then, we provide an application to improve the performance of a generalized linear model. Indeed, we integrate this new knowledge into a generalized linear model to increase the predictive power</subfield>
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      <subfield code="a">Seguro de automóviles</subfield>
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      <subfield code="g">06/12/2021 Número 2 - diciembre 2021 , p. 579-617</subfield>
      <subfield code="t">European Actuarial Journal</subfield>
      <subfield code="d">Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022</subfield>
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