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Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing

Author

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  • Freiman, Jamie W.
  • Kim, Yongbum
  • Vasarhelyi, Miklos A.

Abstract

Changes to the General Ledger (GL) represent a link between transactional business events from Journal Entries and prepared financial statements. Errors in these very large datasets can result in material misstatements or account misbalance. Unfortunately, a plethora of conditions renders traditional statistical and non-statistical sampling less effective. As a full-population examination procedure, Multidimensional Audit Data Sampling (MADS) mitigates these issues. In conjunction with top practitioners, we utilize a design science approach in applying the full-population MADS methodology to a real dataset of GL account balance changes. Issues such as the effectiveness of internal controls, detection of low-frequency high-risk errors, and earnings management concerns are addressed. This paper demonstrates how vital insights can be gained using MADS. More importantly, this approach also highlights the exact portion of the population that is error-free with respect to the auditors' tests.

Suggested Citation

  • Freiman, Jamie W. & Kim, Yongbum & Vasarhelyi, Miklos A., 2022. "Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    • Handle: RePEc:eee:ijoais:v:46:y:2022:i:c:s1467089522000252
    DOI: 10.1016/j.accinf.2022.100573
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    References listed on IDEAS

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    1. Earley, Christine E., 2015. "Data analytics in auditing: Opportunities and challenges," Business Horizons, Elsevier, vol. 58(5), pages 493-500.
    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. Loebbecke, Jk & Neter, J, 1975. "Considerations In Choosing Statistical Sampling Procedures In Auditing," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 13, pages 38-52.
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    Cited by:

    1. Zhou, Yan & Hao, Yinhui, 2025. "Big data tax collection and administration and quality of corporate accounting information," Finance Research Letters, Elsevier, vol. 85(PB).

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