#2253. Robust estimates of insurance misrepresentation through kernel quantile regression mixtures

August 2026publication date
Proposal available till 30-05-2025
5 total number of authors per manuscript3530 $

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Journal’s subject area:
Finance;
Accounting;
Economics and Econometrics;
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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.
Keywords:
big data; insurance claim models; misrepresentation risk assessment; misrepresenter identification; nonparametric regression mixtures

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