Application of Anomaly Detection Techniques to Identify Fraudulent Refunds

19 Pages Posted: 16 Aug 2011

See all articles by Hussein Issa

Hussein Issa

Rutgers, The State University of New Jersey - Accounting & Information Systems

Miklos A. Vasarhelyi

Rutgers Business School

Date Written: August 16, 2011

Abstract

Anomaly detection is a concept widely applied to numerous domains. Several techniques of anomaly detection have been developed over the years, in practice as well as research. The application of this concept has extended to diverse areas, from network intrusion detection to novelty detection in robot behavior. In the business world, the application of these techniques to fraud detection is of a special interest, driven by the great losses companies endure because of such fraudulent activities. This paper describes classification-based and clustering-based anomaly detection techniques and their applications, more specifically the application to the problem of certain fraudulent activities. As an illustration, the paper applies K-Means, a clustering-based algorithm, to a refund transactions dataset from a telecommunication company, with the intent of identifying fraudulent refunds.

Suggested Citation

Issa, Hussein and Vasarhelyi, Miklos A., Application of Anomaly Detection Techniques to Identify Fraudulent Refunds (August 16, 2011). Available at SSRN: https://ssrn.com/abstract=1910468 or http://dx.doi.org/10.2139/ssrn.1910468

Hussein Issa (Contact Author)

Rutgers, The State University of New Jersey - Accounting & Information Systems ( email )

96 New England Avenue, #18
Summit, NJ 07901-1825
United States

Miklos A. Vasarhelyi

Rutgers Business School ( email )

180 University Avenue
Ackerson Hall, Room 315
Newark, NJ 07102
United States
973-353-5002 (Phone)
973-353-1283 (Fax)

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