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Manual journal entry testing: Data analytics and the risk of fraud

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  • Fay, Rebecca
  • Negangard, Eric M.

Abstract

Big Data is revolutionizing the business world as it enables companies to discover valuable insight from within the vast volumes of data now available to companies. The data analysis skills related to Big Data are in high demand, yet many accountants lack the data analysis skills necessary to improve decision making within a company or increase the effectiveness and efficiency of an audit. This case provides you with a hands-on opportunity to utilize the data analysis skills that are in such high demand. You will harness the power of Big Data while performing a procedure that is required on all financial statement audits – an analysis of journal entries for potential red flags of fraud. The case is completed in two phases. First, in Phase I, you will learn how to address one of the primary challenges in the use of Big Data – “validating” the data – which ensures that the files are complete. Specifically, you will import and analyze client files in IDEA to determine if the manual journal entry file is complete or if any records are missing from the file. In Phase II, you will validate data from a second client then perform a battery of tests aimed at identifying potential red flags of fraud. To complete the second task, you will need to consider factors such as who recorded the journal entries, when the entries were created, the description provided (or lack thereof), and whether the entries were back-posted or out-of-balance. You will also learn how to use fuzzy matching and Benford’s Law.

Suggested Citation

  • Fay, Rebecca & Negangard, Eric M., 2017. "Manual journal entry testing: Data analytics and the risk of fraud," Journal of Accounting Education, Elsevier, vol. 38(C), pages 37-49.
    • Handle: RePEc:eee:joaced:v:38:y:2017:i:c:p:37-49
    DOI: 10.1016/j.jaccedu.2016.12.004
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    References listed on IDEAS

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    1. Wallace, Wanda, 2000. "Reporting practices: potential lessons from Cendant Corporation," European Management Journal, Elsevier, vol. 18(3), pages 328-333, June.
    2. 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:

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    2. Stuebs, Martin & Bryant, Scott M. & Edison, Cari & Stanley, Charles, 2022. "Brittney’s Boutique: Tailoring financial statements for function as well as fashion," Journal of Accounting Education, Elsevier, vol. 58(C).
    3. Ballou, Brian & Heitger, Dan L. & Stoel, Dale, 2018. "Data-driven decision-making and its impact on accounting undergraduate curriculum," Journal of Accounting Education, Elsevier, vol. 44(C), pages 14-24.
    4. Lawson, James G. & Street, Daniel A., 2021. "Detecting dirty data using SQL: Rigorous house insurance case," Journal of Accounting Education, Elsevier, vol. 55(C).
    5. Felski, Elizabeth, 2023. "Audit technologies used in practice and ways to implement these technologies into audit courses," Journal of Accounting Education, Elsevier, vol. 62(C).
    6. Francis Aboagye‐Otchere & Cletus Agyenim‐Boateng & Abdulai Enusah & Theodora Ekua Aryee, 2021. "A Review of Big Data Research in Accounting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 268-283, October.
    7. So-Jin Yu & Jin-Sung Rha, 2021. "Research Trends in Accounting Fraud Using Network Analysis," Sustainability, MDPI, vol. 13(10), pages 1-26, May.
    8. Janvrin, Diane J. & Weidenmier Watson, Marcia, 2017. "“Big Data”: A new twist to accounting," Journal of Accounting Education, Elsevier, vol. 38(C), pages 3-8.

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