Unsupervised Profiling for Identifying Superimposed Fraud

  • Uzi Murad
  • Gadi Pinkas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1704)


Many fraud analysis applications try to detect “probably fraudulent” usage patterns, and to discover these patterns in historical data. This paper builds on a different detection concept; there are no fixed “probably fraudulent” patterns, but any significant deviation from the normal behavior indicates a potential fraud. In order to detect such deviations, a comprehensive representation of “customer behavior” must be used. This paper presents such representation, and discusses issues derived from it: a distance function and a clustering algorithm for probability distributions.


False Alarm Rate Customer Behavior Call Duration Fraud Detection Fraud Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Uzi Murad
    • 1
  • Gadi Pinkas
    • 2
  1. 1.Tel Aviv UniversityRamat-AvivIsrael
  2. 2.Amdocs (Israel) Ltd.Ra’ananaIsrael

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