Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing |
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| Affiliation: | 1. Marist College, United States;2. Ramapo College, United States;3. Rutgers University, United States;1. College of Business Administration, University of Seoul, Seoulsiripdaero 163, Dongdaemun-gu, Seoul 02504, South Korea;2. Shidler College of Business, University of Hawaii at Manoa, 2404 Maile Way, Honolulu, HI 96822, United States;3. School of Management, Clark University, 950 Main Street, Worcester, MA 01610, United States;1. Florida State University, 1107 W. Call St., Tallahassee, FL 32306-4301, United States;2. Grand Valley State University, 3032 L William Seidman Center, 50 Front Ave SW, Grand Rapids, MI 49504-6424, United States;3. University of Tampa, JS 243, Mailbox O, 401 W. Kennedy Blvd., Tampa, FL 33606, United States;4. Florida State University, 346 RBB, 821 Academic Way, Tallahassee, FL 32306-1110, United States;5. Michigan State University, 632 Bogue St. Rm N220, East Lansing, MI 48824, United States;1. Dept. of Business Informatics, Hanyang University, Seoul, Korea;2. School of Business, Hanyang University, Seoul, Korea;1. University of Waterloo, School of Accounting and Finance, 200 University Ave W, Waterloo, ON N2L 3G1, Canada;2. California State University Long Beach, College of Business, 1250 N Bellflower Blvd, Long Beach, CA 90815, USA;1. University of Northern Colorado, United States;2. University of Northern Colorado and Monash University, United States and Australia;3. Oregon State University, United States;4. Virginia Commonwealth University, United States |
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| 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. |
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| Keywords: | Audit Big data General ledger Audit analytics Full population testing Suspicion scoring |
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