A Model for the Detection of Insurance Fraud

The aim of this article is to develop a model to aid insurance companies in their decision-making and to ensure that they are better equipped to fight fraud. This tool is based on the systematic use of fraud indicators. We first propose a procedure to isolate the indicators which are most significant in predicting the probability that a claim may be fraudulent. We applied the procedure to data collected in the Dionne Belhadji study (1996). The model allowed us to observe that 23 of the 54 indicators used were significant in predicting the probability of fraud. Our study also discusses the model's accuracy and detection capability. The detection rates obtained by the adjusters who participated in the study constitute the reference point of this discussion. As shown in the Caron-Dionne (1998), there is the possibility that these rates underestimate the true level of fraud