A Robust Unsupervised Method for Fraud Rate Estimation

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