Fraud Classification Using Principal Component Analysis of Ridits

31 Pages Posted: 21 Mar 2003

See all articles by Patrick L. Brockett

Patrick L. Brockett

University of Texas at Austin - Department of Information, Risk and Operations Management

Richard A. Derrig

Automobile Insurers Bureau of Massachusetts

Linda L. Golden

University of Texas at Austin - Red McCombs School of Business

Arnold Levine

Tulane University - Department of Mathematics

Mark Alpert

University of Texas at Austin - Red McCombs School of Business

Abstract

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.

Suggested Citation

Brockett, Patrick L. and Derrig, Richard A. and Golden, Linda L. and Levine, Arnold and Alpert, Mark, Fraud Classification Using Principal Component Analysis of Ridits. Available at SSRN: https://ssrn.com/abstract=342800

Patrick L. Brockett (Contact Author)

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States

Richard A. Derrig

Automobile Insurers Bureau of Massachusetts ( email )

101 Arch Street
Boston, MA 02110
United States
617-439-4542 (Phone)
617-439-6789 (Fax)

Linda L. Golden

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

Arnold Levine

Tulane University - Department of Mathematics

6823 St Charles Ave
New Orleans, LA 70118
United States

Mark Alpert

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

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