A Robust Unsupervised Method for Fraud Rate Estimation

Journal of Risk and Insurance, Forthcoming

34 Pages Posted: 15 May 2011 Last revised: 20 Aug 2012

See all articles by Jing Ai

Jing Ai

University of Hawaii at Manoa - Shidler College of Business

Patrick L. Brockett

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

Linda L. Golden

University of Texas at Austin - Red McCombs School of Business

Montserrat Guillen

Multiple version iconThere are 2 versions of this paper

Date Written: January 18, 2011

Abstract

If one is interested in managing fraud, one must measure the fraud rate to be able to assess the degree of the problem and the effectiveness of the fraud management technique. This paper offers a robust new method for estimating fraud rate, PRIDIT-FRE (PRIDIT-based Fraud Rate Estimation), developed based on PRIDIT, an unsupervised fraud detection method to assess individual claim fraud suspiciousness. PRIDIT-FRE presents the first nonparametric unsupervised estimator of the actual rate of fraud in a population of claims, robust to the bias contained in the audited sample (arising from the quality or individual hubris of an auditor or investigator, or the natural data gathering process through claims adjusting). PRIDIT-FRE exploits the internal consistency of fraud predictors and makes use of a small audited sample or even an unaudited sample to obtain an estimate of the fraud rate. Using two insurance fraud data sets with different characteristics, we illustrate the effectiveness of PRIDIT-FRE and examine its robustness in varying scenarios.

Keywords: fraud rate, fraud detection, insurance fraud, unsupervised learning, PRIDIT, PRIDIT-FRE, audit

Suggested Citation

Ai, Jing and Brockett, Patrick L. and Golden, Linda L. and Guillen, Montserrat, A Robust Unsupervised Method for Fraud Rate Estimation (January 18, 2011). Journal of Risk and Insurance, Forthcoming. Available at SSRN: https://ssrn.com/abstract=1841809

Jing Ai (Contact Author)

University of Hawaii at Manoa - Shidler College of Business ( email )

2404 Maile Way
Honolulu, HI 96822
United States

Patrick L. Brockett

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

CBA 5.202
Austin, TX 78712
United States

Linda L. Golden

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

Austin, TX 78712
United States

No contact information is available for Montserrat Guillen

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