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Robust estimates of insurance misrepresentation through kernel quantile regression mixtures

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  • Hong Li
  • Qifan Song
  • Jianxi Su

Abstract

This paper pertains to a class of nonparametric methods for studying the misrepresentation issue in insurance applications. For this purpose, mixture models based on quantile regression in reproducing kernel Hilbert spaces are employed. Compared with the existing parametric approaches, the proposed framework features a more flexible statistics structure which could alleviate the risk of model misspecification, and is in the meantime more robust to outliers in the data. The proposed framework can not only estimate the prevalence of misrepresentation in the data, but also help identify the most suspicious individuals for the validation purpose. Through embedding state‐of‐the‐art machine learning techniques, we present a novel statistics procedure to efficiently estimate the proposed misrepresentation model in the presence of massive data. The proposed methodology is applied to study the Medical Expenditure Panel Survey data, and a significant degree of misrepresentation activity is found on the self‐reported insurance status.

Suggested Citation

  • Hong Li & Qifan Song & Jianxi Su, 2021. "Robust estimates of insurance misrepresentation through kernel quantile regression mixtures," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 625-663, September.
    • Handle: RePEc:bla:jrinsu:v:88:y:2021:i:3:p:625-663
    DOI: 10.1111/jori.12358
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    References listed on IDEAS

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    Cited by:

    1. Michele Tumminello & Andrea Consiglio & Pietro Vassallo & Riccardo Cesari & Fabio Farabullini, 2023. "Insurance fraud detection: A statistically validated network approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(2), pages 381-419, June.
    2. Daniel Bauer & James Tyler Leverty & Joan Schmit & Justin Sydnor, 2021. "Symposium on insure‐tech, digitalization, and big‐data techniques in risk management and insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 525-528, September.

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