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Adaptive Fraud Detection Using Benford’s Law

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 4013)

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.

Keywords

  • Optimal Policy
  • Outlier Detection
  • Blind Test
  • Fraud Detection
  • Digit Frequency

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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References

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© 2006 Springer-Verlag Berlin Heidelberg

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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. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_30

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  • DOI: https://doi.org/10.1007/11766247_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34628-9

  • Online ISBN: 978-3-540-34630-2

  • eBook Packages: Computer ScienceComputer Science (R0)