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

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

International Journal of Accounting Information Systems, 2022, vol. 46, issue C

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.

Keywords: Audit; Big data; General ledger; Audit analytics; Full population testing; Suspicion scoring (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1467089522000252
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:46:y:2022:i:c:s1467089522000252

DOI: 10.1016/j.accinf.2022.100573

Access Statistics for this article

International Journal of Accounting Information Systems is currently edited by S.V. Grabski

More articles in International Journal of Accounting Information Systems from Elsevier
Bibliographic data for series maintained by Catherine Liu ().