A Robust Unsupervised Method for Fraud Rate Estimation

Authors

  • Jing Ai,

    1. Jing Ai is an Assistant Professor of Risk Management and Insurance at the Department of Financial Economics and Institutions, Shidler College of Business, The University of Hawaii at Manoa, Honolulu, Hawaii. Patrick L. Brockett is the Gus S. Wortham Chaired Professor in Risk Management and Insurance at the Department of Information, Risk, and Operations Management, Red McCombs School of Business, The University of Texas at Austin, Austin, Texas. Linda L. Golden is the Marlene and Morton Meyerson Centennial Professor in Business at Department of Marketing, Red McCombs School of Business, The University of Texas at Austin, Austin, Texas. Montserrat Guillén is a Chaired Professor in Economics at Department of Econometrics, University of Barcelona, Barcelona, Spain. The authors can be contacted via e-mail: jing.ai@hawaii.edu, brockett@mail.utexas.edu, utlindagolden@gmail.com, and mguillen@ub.edu, respectively. Jing Ai is the contact author.
    Search for more papers by this author
  • Patrick L. Brockett,

    1. Jing Ai is an Assistant Professor of Risk Management and Insurance at the Department of Financial Economics and Institutions, Shidler College of Business, The University of Hawaii at Manoa, Honolulu, Hawaii. Patrick L. Brockett is the Gus S. Wortham Chaired Professor in Risk Management and Insurance at the Department of Information, Risk, and Operations Management, Red McCombs School of Business, The University of Texas at Austin, Austin, Texas. Linda L. Golden is the Marlene and Morton Meyerson Centennial Professor in Business at Department of Marketing, Red McCombs School of Business, The University of Texas at Austin, Austin, Texas. Montserrat Guillén is a Chaired Professor in Economics at Department of Econometrics, University of Barcelona, Barcelona, Spain. The authors can be contacted via e-mail: jing.ai@hawaii.edu, brockett@mail.utexas.edu, utlindagolden@gmail.com, and mguillen@ub.edu, respectively. Jing Ai is the contact author.
    Search for more papers by this author
  • Linda L. Golden,

    1. Jing Ai is an Assistant Professor of Risk Management and Insurance at the Department of Financial Economics and Institutions, Shidler College of Business, The University of Hawaii at Manoa, Honolulu, Hawaii. Patrick L. Brockett is the Gus S. Wortham Chaired Professor in Risk Management and Insurance at the Department of Information, Risk, and Operations Management, Red McCombs School of Business, The University of Texas at Austin, Austin, Texas. Linda L. Golden is the Marlene and Morton Meyerson Centennial Professor in Business at Department of Marketing, Red McCombs School of Business, The University of Texas at Austin, Austin, Texas. Montserrat Guillén is a Chaired Professor in Economics at Department of Econometrics, University of Barcelona, Barcelona, Spain. The authors can be contacted via e-mail: jing.ai@hawaii.edu, brockett@mail.utexas.edu, utlindagolden@gmail.com, and mguillen@ub.edu, respectively. Jing Ai is the contact author.
    Search for more papers by this author
  • Montserrat Guillén

    1. Jing Ai is an Assistant Professor of Risk Management and Insurance at the Department of Financial Economics and Institutions, Shidler College of Business, The University of Hawaii at Manoa, Honolulu, Hawaii. Patrick L. Brockett is the Gus S. Wortham Chaired Professor in Risk Management and Insurance at the Department of Information, Risk, and Operations Management, Red McCombs School of Business, The University of Texas at Austin, Austin, Texas. Linda L. Golden is the Marlene and Morton Meyerson Centennial Professor in Business at Department of Marketing, Red McCombs School of Business, The University of Texas at Austin, Austin, Texas. Montserrat Guillén is a Chaired Professor in Economics at Department of Econometrics, University of Barcelona, Barcelona, Spain. The authors can be contacted via e-mail: jing.ai@hawaii.edu, brockett@mail.utexas.edu, utlindagolden@gmail.com, and mguillen@ub.edu, respectively. Jing Ai is the contact author.
    Search for more papers by this author

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 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.

Ancillary