Detecting fraud in health insurance data: learning to model incomplete benford's law distributions

Authors:
Fletcher Lu;J. Efrim Boritz
Affiliations:
School of Computer Science, University of Waterloo & Canadian Institute of Chartered Accountants, Scarborough, Ontario, Canada;School of Accountancy, University of Waterloo, Waterloo, Ontario, Canada
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

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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.