Application of Anomaly Detection Techniques to Identify Fraudulent Refunds

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.