IDEAS home Printed from https://ideas.repec.org/a/cmj/seapas/y2016i12p561-566.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://seaopenresearch.eu/Journals/articles/SPAS_12_19.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    2. de Araújo Silva, Archibald & Aparecida Gouvêa, Maria, 2023. "Study on the effect of sample size on type I error, in the first, second and first-two digits excessmad tests," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    3. Montag, Josef, 2017. "Identifying odometer fraud in used car market data," Transport Policy, Elsevier, vol. 60(C), pages 10-23.
    4. Montag, Josef, 2015. "Identifying Odometer Fraud: Evidence from the Used Car Market in the Czech Republic," MPRA Paper 65182, University Library of Munich, Germany.
    5. Alles, Michael & Gray, Glen L., 2016. "Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 44-59.
    6. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    7. Andreas Quatember, 2019. "A discussion of the two different aspects of privacy protection in indirect questioning designs," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 269-282, January.
    8. Chen, Yuh-Jen & Liou, Wan-Ching & Chen, Yuh-Min & Wu, Jyun-Han, 2019. "Fraud detection for financial statements of business groups," International Journal of Accounting Information Systems, Elsevier, vol. 32(C), pages 1-23.
    9. Yuan Song & Hongwei Wang & Maoran Zhu, 2018. "Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-14, December.
    10. Laskai András, 2019. "AI foundations of the international business planning and the AI consciousness model," International Journal of Science and Business, IJSAB International, vol. 3(1), pages 17-28.
    11. Dlugosz, Stephan & Müller-Funk, Ulrich, 2012. "Ziffernanalyse zur Betrugserkennung in Finanzverwaltungen: Prüfung von Kassenbelegen," Arbeitsberichte des Instituts für Wirtschaftsinformatik 133, University of Münster, Department of Information Systems.
    12. Earley, Christine E., 2015. "Data analytics in auditing: Opportunities and challenges," Business Horizons, Elsevier, vol. 58(5), pages 493-500.
    13. Andrea Cardoni & Evgeniia Kiseleva & Francesco De Luca, 2020. "Continuous auditing and data mining for strategic risk control and anticorruption: Creating “fair” value in the digital age," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3072-3085, December.
    14. Diekmann Andreas & Jann Ben, 2010. "Benford’s Law and Fraud Detection: Facts and Legends," German Economic Review, De Gruyter, vol. 11(3), pages 397-401, August.
    15. 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.
    16. Lee, Kang-Bok & Han, Sumin & Jeong, Yeasung, 2020. "COVID-19, flattening the curve, and Benford’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    17. Kishore Singh & Peter Best, 2020. "Implementing Benford’s Law in Continuous Monitoring Applications," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 19(2), pages 379-404, June.
    18. Saxton, Gregory D. & Guo, Chao, 2020. "Social media capital: Conceptualizing the nature, acquisition, and expenditure of social media-based organizational resources," International Journal of Accounting Information Systems, Elsevier, vol. 36(C).
    19. Maria Tragouda & Michalis Doumpos & Constantin Zopounidis, 2024. "Identification of fraudulent financial statements through a multi‐label classification approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    20. 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.

    More about this item

    Keywords

    Benford’s Law; Sectoral Analysis; Mean Absolute Deviation;
    All these keywords.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cmj:seapas:y:2016:i:12:p:561-566. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Serghie Dan (email available below). General contact details of provider: https://seaopenresearch.eu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.