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An introductory audit data analytics case study: Using Microsoft Power BI and Benford’s Law to detect accounting irregularities

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  • Nickell, Erin Burrell
  • Schwebke, Jason
  • Goldwater, Paul

Abstract

This case introduces you to the use of data analytics in accounting for purposes of identifying irregularities in a large data set of invoices using Microsoft Power BI. The goal of this case is two-fold. First, the case provides you a guided approach to navigating Power BI at the beginner level. Second, the case serves as an introduction to the use of data analytics in an auditing context for purposes of identifying irregularities in a large data set of invoices. You will evaluate the data set according to Benford’s Law and create an interactive “dashboard” visualization to present the results of your analysis to a supervisor. Additionally, you will document your findings in a written report according to professional auditing standards. We provide evidence of case efficacy in both a graduate-level fraud auditing course as well as an undergraduate accounting information systems (AIS) course with a data analytics focus. The case is suitable as an introductory data analytics assignment in any course with an auditing, fraud, forensics, AIS, or data analytics focus where students have little or no prior experience with Power BI. The case may also be used as an introduction to Benford’s Law as students are not required to have prior experience with Benford’s Law in order to complete the assignment.

Suggested Citation

  • Nickell, Erin Burrell & Schwebke, Jason & Goldwater, Paul, 2023. "An introductory audit data analytics case study: Using Microsoft Power BI and Benford’s Law to detect accounting irregularities," Journal of Accounting Education, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:joaced:v:64:y:2023:i:c:s0748575123000271
    DOI: 10.1016/j.jaccedu.2023.100855
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    References listed on IDEAS

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    1. Andiola, Lindsay M. & Masters, Erin & Norman, Carolyn, 2020. "Integrating technology and data analytic skills into the accounting curriculum: Accounting department leaders’ experiences and insights," Journal of Accounting Education, Elsevier, vol. 50(C).
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    Cited by:

    1. De Clercq, Djavan & Mahdi, Adam, 2024. "Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis and remote sensing data," Agricultural Systems, Elsevier, vol. 220(C).
    2. Koh, Wei Chern & Yee, Tommy C.T. & Yeow, Adrian Y.K., 2024. "Teaching case: To be a goods and services tax-registered business or not – Decision making using analytics☆," Journal of Accounting Education, Elsevier, vol. 67(C).

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