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.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Benford, F.: The Law of Anomalous Numbers. In: Proceedings of the American Philosophical Society, pp. 551–571 (1938)
Crowder, N.: Fraud Detection Techniques. Internal Auditor, 17–20 (April 1997)
Pinkham, R.S.: On the Distribution of First Significant Digits. Annals of Mathematical Statistics 32, 1223–1230 (1961)
Hill, T.P.: A Statistical Derivation of the Significant-Digit Law. Statistical Science 4, 354–363 (1996)
Carslaw, C.A.: Anomalies in Income Numbers: Evidence of Goal Oriented Behaviour. The Accounting Review 63, 321–327 (1988)
Busta, B., Weinberg, R.: Using Benford’s Law and neural networks as a review procedure. Managerial Auditing Journal, 266–356 (1998)
Fawcett, T.: AI Approaches to Fraud Detection & Risk Management. Technical Report WS-97-07, AAAI Workshop: Technical Report (1997)
Bolton, R.J., Hand, D.J.: Statistical Fraud Detection: A Review. Statistical Science 17(3), 235–255 (1999)
Nigrini, M.J.: Digital Analysis Using Benford’s Law. Global Audit Publications, Vancouver (2000)
Nigrini, M.J., Mittermaier, L.J.: The Use of Benford’s Law as an Aid in Analytical Procedures. Auditing: A Journal of Practice and Theory 16(2), 52–67 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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. https://doi.org/10.1007/11564096_63
Download citation
DOI: https://doi.org/10.1007/11564096_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29243-2
Online ISBN: 978-3-540-31692-3
eBook Packages: Computer ScienceComputer Science (R0)