FRAUD CLASSIFICATION USING PRINCIPAL COMPONENT ANALYSIS OF RIDITs

This article introduces to the statistical and insurance literature a mathematical technique for an a priori classification of objects when no training sample exists for which the exact correct group membership is known. The article also provides an example of the empirical application of the methodology to fraud detection for bodily injury claims in automobile insurance. With this technique, principal component analysis of RIDIT scores (PRIDIT), an insurance fraud detector can reduce uncertainty and increase the chances of targeting the appropriate claims so that an organization will be more likely to allocate investigative resources efficiently to uncover insurance fraud. In addition, other (exogenous) empirical models can be validated relative to the PRIDIT-derived weights for optimal ranking of fraud/nonfraud claims and/or profiling. The technique at once gives measures of the individual fraud indicator variables’ worth and a measure of individual claim file suspicion level for the entire claim file that can be used to cogently direct further fraud investigation resources. Moreover, the technique does so at a lower cost than utilizing human insurance investigators, or insurance adjusters, but with similar outcomes. More generally, this technique is applicable to other commonly encountered managerial settings in which a large number of assignment decisions are made subjectively based on “clues,” which may change dramatically over time. This article explores the application of these techniques to injury insurance claims for automobile bodily injury in detail. Patrick Brockett is the Gus S. Wortham chaired professor of risk management at the University of Texas at Austin. Richard Derrig is senior vice president, Automobile Insurers Bureau of Massachusetts and vice president of research, Insurance Fraud Bureau of Massachusetts. Linda Golden is the Marlene and Morton Meyerson Centennial Professor in Business at the University of Texas. Arnold Levine is professor emeritus in the Department of Mathematics, Tulane University. Mark Alpert is a professor of marketing at the University of Texas.