Robust estimates of insurance misrepresentation through kernel quantile regression mixtures

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.

Insurance fraud detection: A statistically validated network approach

“…Clearly, we are aware that a statistical anomaly cannot be considered a sentence of guilt. However, on the one hand, statistical anomalies are already used in the literature to identify suspicious activity in the insurance sector, for example, Li et al (2021) propose a nonparametric method for studying the misrepresentation in insurance data, which also helps spot suspicious individuals for the validation purpose. On the other hand, information about structures with very strong ties in the network is vital for investigating units as it strongly reduces the-virtually-uncountable number of structures, and, therefore, the cost and the time needed to liquidate honest claimants.…”