A Predictive Modeling for Detecting Fraudulent Automobile Insurance Claims

Abstract

Fraudulent automobile insurance claims are not only a loss for insurance companies, but also for their policyholders. The goal of this research is to develop, first, a decision-making algorithm to classify whether a claim is classified as fraudulent or not; and, second, what types of variables should be focused to detect fraudulent claims. To achieve this goal, highly accurate prediction models are built by discovering important sets of features via variable selection algorithms, which can in turn help prevent future loss. In this research, parametric and nonparametric statistical learning algorithms are considered to reduce uncertainty and increase the chances of detecting the appropriate claims. An important set of features for a model is determined by measuring variable importance based on the observed characteristics of a claim via a cross-validation and by testing improvement of the performance at which automobile fraudulent claims are accurately classified using Akaike Information Criterion. We could achieve accuracy above 95% with a set of features selected via a cross-validation. This research would offer some benefit to the insurance industry for their fraud detection research in order to prevent insurance abuse from escalating any further.