Adaptive Fraud Detection Using Benford’s Law

  • Fletcher Lu
  • J. Efrim Boritz
  • Dominic Covvey
Conference paper

DOI: 10.1007/11766247_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4013)
Cite this paper as:
Lu F., Boritz J.E., Covvey D. (2006) Adaptive Fraud Detection Using Benford’s Law. In: Lamontagne L., Marchand M. (eds) Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science, vol 4013. Springer, Berlin, Heidelberg

Abstract

Adaptive Benford’s Law [1] is a digital analysis technique that specifies the probabilistic distribution of digits for many commonly occurring phenomena, even for incomplete data records. We combine this digital analysis technique with a reinforcement learning technique to create a new fraud discovery approach. When applied to records of naturally occurring phenomena, our adaptive fraud detection method uses deviations from the expected Benford’s Law distributions as an indicators of anomalous behaviour that are strong indicators of fraud. Through the exploration component of our reinforcement learning method we search for the underlying attributes producing the anomalous behaviour. In a blind test of our approach, using real health and auto insurance data, our Adaptive Fraud Detection method successfully identified actual fraudsters among the test data.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fletcher Lu
    • 1
  • J. Efrim Boritz
    • 2
  • Dominic Covvey
    • 2
  1. 1.Canadian Institute of Chartered AccountantsScarborough, Ontario
  2. 2.University of WaterlooWaterloo, OntarioCanada

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