Insurance ratemaking using a combined quantile regression machine learning approach

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<title>Insurance ratemaking using a combined quantile regression machine learning approach</title>
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<namePart>Kacem, Manel</namePart>
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<namePart>Belkacem, Lotfi</namePart>
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<abstract displayLabel="Summary">The article analyzes the process of ratemaking in non-life insurance through the combination of quantile regression and machine learning techniques. A two-stage approach is proposed that first models the probability of a claim and subsequently the aggregated claim amount, using advanced models such as random forest, gradient boosting, XGBoost, and neural networks applied to quantile regression. The empirical study, based on real automobile insurance data, shows that these models allow for a better capture of extreme risks and improve the accuracy of premium estimation. In addition, model performance is evaluated using statistical metrics, and practical implications for actuarial ratemaking are discussed</abstract>
<note type="statement of responsibility">Souleima Zanzouri, Manel Kacem and Lotfi Belkacem</note>
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<topic>Tarificación</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080573935">
<topic>Seguros no vida</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080592011">
<topic>Modelos actuariales</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080581886">
<topic>Primas de seguros</topic>
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<topic>Machine learning</topic>
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<title>North American actuarial journal</title>
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<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
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<text>16/03/2026 Tomo 30 Número 1 - 2026 , 51 p.</text>
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