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Benford’S Law In The Case Of Hungarian Whole-Sale Trade Sector

Author

Listed:
  • Rabeea SADAF

    (Károly Ihrig Doctoral School of Management and Business, Debrecen University)

Abstract

Benford’s law has attracted many researchers for detecting the fraudulent data and can be used as one of the digital analysis tools for auditing of the accounting data. In this treatise, the accuracy of figures reported in Hungarian Trading Companies’ data are examined through digital analysis technique with the consideration of Benford’s Law. The net sales data from the period of year 2009 to 2014 has been used for detecting the anomalies and to confirm whether the digit-pattern follows Benford’s distribution. Through the obtained results we claimed that the frequencies of first and second digits’ place follow the Benford’s theoretical distribution and exhibits to close conformity. Moreover analysis of the second, first-order and second-order gave a mixed result of close conformity to significant deviation from expected frequency. Also the absolute deviation (MAD) value of first and second digit suggest an overall conformity of the data to Benford’s distribution.

Suggested Citation

  • Rabeea SADAF, 2016. "Benford’S Law In The Case Of Hungarian Whole-Sale Trade Sector," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 12, pages 561-566, December.
    • Handle: RePEc:cmj:seapas:y:2016:i:12:p:561-566
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    References listed on IDEAS

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    1. 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.
    2. Gray, Glen L. & Debreceny, Roger S., 2014. "A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 15(4), pages 357-380.
    3. Christoph Watrin & Ralf Struffert & Robert Ullmann, 2008. "Benford’s Law: an instrument for selecting tax audit targets?," Review of Managerial Science, Springer, vol. 2(3), pages 219-237, November.
    4. Debreceny, Roger S. & Gray, Glen L., 2010. "Data mining journal entries for fraud detection: An exploratory study," International Journal of Accounting Information Systems, Elsevier, vol. 11(3), pages 157-181.
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    Cited by:

    1. Rabeea Sadaf, 2017. "Advanced Statistical Techniques For Testing Benford'S Law," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 229-238, December.
    2. Hamida, Amal Ben & de Peretti, Christian & Belkacem, Lotfi, 2026. "Benford’s law and intraday microstructure anomalies: Forecasting market movements with high-frequency data," Research in International Business and Finance, Elsevier, vol. 84(C).

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    More about this item

    Keywords

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    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • M40 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - General

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