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

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<title>Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data</title>
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<namePart>Maillart, Arthur</namePart>
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<abstract displayLabel="Summary">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</abstract>
<note type="statement of responsibility">Arthur Maillart</note>
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<topic>Machine learning</topic>
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<topic>Seguro de automóviles</topic>
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<topic>Telemática</topic>
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<topic>Data driven</topic>
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<title>European Actuarial Journal</title>
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<publisher>Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022</publisher>
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<identifier type="local">MAP20220007085</identifier>
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<text>06/12/2021 Número 2 - diciembre 2021 , p. 579-617</text>
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