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Benford's Law and Fraud Detection: Facts and Legends

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  • Andreas Diekmann
  • Ben Jann

Abstract

. Is Benford's law a good instrument to detect fraud in reports of statistical and scientific data? For a valid test, the probability of ‘false positives’ and ‘false negatives’ has to be low. However, it is very doubtful whether the Benford distribution is an appropriate tool to discriminate between manipulated and non‐manipulated estimates. Further research should focus more on the validity of the test and test results should be interpreted more carefully.

Suggested Citation

  • Andreas Diekmann & Ben Jann, 2010. "Benford's Law and Fraud Detection: Facts and Legends," German Economic Review, Verein für Socialpolitik, vol. 11(3), pages 397-401, August.
    • Handle: RePEc:bla:germec:v:11:y:2010:i:3:p:397-401
    DOI: 10.1111/j.1468-0475.2010.00510.x
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    References listed on IDEAS

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    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. Dewald, William G & Thursby, Jerry G & Anderson, Richard G, 1986. "Replication in Empirical Economics: The Journal of Money, Credit and Banking Project," American Economic Review, American Economic Association, vol. 76(4), pages 587-603, September.
    3. Andreas Diekmann, 2007. "Not the First Digit! Using Benford's Law to Detect Fraudulent Scientif ic Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(3), pages 321-329.
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    Cited by:

    1. Bernhard Rauch & Max G�ttsche & Stephan Langenegger, 2014. "Detecting Problems in Military Expenditure Data Using Digital Analysis," Defence and Peace Economics, Taylor & Francis Journals, vol. 25(2), pages 97-111, April.
    2. Cunjak Mataković Ivana, 2019. "The empirical analysis of financial reports of companies in Croatia: Benford distribution curve as a benchmark for first digits," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 5(2), pages 90-100, December.
    3. Shikano Susumu & Mack Verena, 2011. "When Does the Second-Digit Benford’s Law-Test Signal an Election Fraud?: Facts or Misleading Test Results," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(5-6), pages 719-732, October.
    4. Piotr Lityński & Artur Hołuj, 2020. "Urban Sprawl Risk Delimitation: The Concept for Spatial Planning Policy in Poland," Sustainability, MDPI, vol. 12(7), pages 1-19, March.
    5. Horton, Joanne & Krishna Kumar, Dhanya & Wood, Anthony, 2020. "Detecting academic fraud using Benford law: The case of Professor James Hunton," Research Policy, Elsevier, vol. 49(8).
    6. Florian El Mouaaouy & Jan Riepe, 2018. "Benford and the Internal Capital Market: A Useful Indicator of Managerial Engagement," German Economic Review, Verein für Socialpolitik, vol. 19(3), pages 309-329, August.
    7. Pankaj C. Patel & Mike G. Tsionas & Maria João Guedes, 2022. "Benford's law, small business financial reporting, and survival," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(8), pages 3301-3315, December.
    8. Druică, Elena & Oancea, Bogdan & Vâlsan, Călin, 2018. "Benford's law and the limits of digit analysis," International Journal of Accounting Information Systems, Elsevier, vol. 31(C), pages 75-82.
    9. El Mouaaouy Florian & Riepe Jan, 2018. "Benford and the Internal Capital Market: A Useful Indicator of Managerial Engagement," German Economic Review, De Gruyter, vol. 19(3), pages 309-329, August.
    10. Venuka Aggarwal & Khushdeep Dharni, 2020. "Deshelling the Shell Companies Using Benford’s Law: An Emerging Market Study," Vikalpa: The Journal for Decision Makers, , vol. 45(3), pages 160-169, September.
    11. Justin T. Pickett, 2020. "The Stewart Retractions: A Quantitative and Qualitative Analysis," Econ Journal Watch, Econ Journal Watch, vol. 17(1), pages 152–190-1, March.
    12. Faraji Kasidi & H. Chaturvedi & Rahul Singh, 2010. "Detecting Data Error and Inaccuracy," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(4), pages 405-425, November.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions

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