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A Robust Unsupervised Method for Fraud Rate Estimation
Author(s) -
Ai Jing,
Brockett Patrick L.,
Golden Linda L.,
Guillén Montserrat
Publication year - 2013
Publication title -
journal of risk and insurance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.055
H-Index - 63
eISSN - 1539-6975
pISSN - 0022-4367
DOI - 10.1111/j.1539-6975.2012.01467.x
Subject(s) - robustness (evolution) , nonparametric statistics , audit , computer science , estimator , hubris , population , sample (material) , estimation , consistency (knowledge bases) , econometrics , actuarial science , statistics , accounting , business , artificial intelligence , economics , mathematics , history , biochemistry , chemistry , demography , management , chromatography , sociology , gene , classics
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 article 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 an 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 an unaudited sample only. Using two insurance fraud data sets with different characteristics, we illustrate the effectiveness of PRIDIT‐FRE and examine its robustness in varying scenarios.