Embedded Predictive Analysis of Misrepresentation Risk in GLM Ratemaking Models

By Michelle Xia, Lei Hua, Gary James Vadnais

Download PDF of Full Text

Abstract

Misrepresentation is a type of insurance fraud that happens frequently in policy applications. Due to the unavailability of data, such frauds are usually expensive or difficult to detect. Based on the distributional structure of regular ratemaking data, we propose a generalized linear model (GLM) framework that allows for an embedded predictive analysis on the misrepresentation risk. In particular, we treat binary misrepresentation indicators as latent variables under GLM ratemaking models for rating factors that are subject to misrepresentation. Based on a latent logistic regression model on the prevalence of misrepresentation, the model identifies characteristics of policies that are subject to a high risk of misrepresentation. The method allows for multiple factors that are subject to misrepresentation, while accounting for other correctly measured risk factors. Based on the observed variables on the claim outcome and rating factors, we derive a mixture regression model structure that possesses identifiability. The identifiability ensures valid inference on the parameters of interest, including the rating relativities and the prevalence of misrepresentation. The usefulness of the method is demonstrated by simulation studies, as well as a case study using the Medical Expenditure Panel Survey data.

Keywords Misrepresentation, ratemaking, predictive analysis, generalized linear models, Bayesian inference, Markov chain Monte Carlo

Citation

Xia, Michelle, Lei Hua, and Gary James Vadnais, "Embedded Predictive Analysis of Misrepresentation Risk in GLM Ratemaking Models," Variance 12:1, 2018, pp. 39-58.

Taxonomy Classifications


Subscribe to the RSS Feed

Email List

Sign up today for the Variance e-mail list and receive updates about new issues, articles, and special features.

Mission Statement

Variance (ISSN 1940-6452) is a peer-reviewed journal published by the Casualty Actuarial Society to disseminate work of interest to casualty actuaries worldwide. The focus of Variance is original practical and theoretical research in casualty actuarial science. Significant survey or similar articles are also considered for publication. Membership in the Casualty Actuarial Society is not a prerequisite for submitting papers to the journal and submissions by non-CAS members is encouraged.