Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford’s Law Distributions

  • Fletcher Lu
  • J. Efrim Boritz
Conference paper

DOI: 10.1007/11564096_63

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3720)
Cite this paper as:
Lu F., Boritz J.E. (2005) Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford’s Law Distributions. In: Gama J., Camacho R., Brazdil P.B., Jorge A.M., Torgo L. (eds) Machine Learning: ECML 2005. ECML 2005. Lecture Notes in Computer Science, vol 3720. Springer, Berlin, Heidelberg

Abstract

Benford’s Law [1] specifies the probabilistic distribution of digits for many commonly occurring phenomena, ideally when we have complete data of the phenomena. We enhance this digital analysis technique with an unsupervised learning method to handle situations where data is incomplete. We apply this method to the detection of fraud and abuse in health insurance claims using real health insurance data. We demonstrate improved precision over the traditional Benford approach in detecting anomalous data indicative of fraud and illustrate some of the challenges to the analysis of healthcare claims fraud.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Fletcher Lu
    • 1
  • J. Efrim Boritz
    • 2
  1. 1.School of Computer ScienceUniversity of Waterloo & Canadian Institute of Chartered AccountantsScarboroughCanada
  2. 2.School of AccountancyUniversity of WaterlooWaterlooCanada

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