Chapter

Advances in Artificial Intelligence

Volume 4013 of the series Lecture Notes in Computer Science pp 347-358

Adaptive Fraud Detection Using Benford’s Law

  • Fletcher LuAffiliated withCanadian Institute of Chartered Accountants
  • , J. Efrim BoritzAffiliated withUniversity of Waterloo
  • , Dominic CovveyAffiliated withUniversity of Waterloo

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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.