Outlier Detection in Auditing: Integrating Unsupervised Learning within a Multilevel Framework for General Ledger Analysis

Wei, Danyang; Cho, Soohyun; Vasarhelyi, Miklos A; Liam, Te-Wierik

Abstract

Auditors traditionally use sampling techniques to examine general ledger (GL) data, which suffer from sampling risks. Hence, recent research proposes full-population testing techniques, such as suspicion scoring, which rely on auditors' judgment to recognize possible risk factors and develop corresponding risk filters to identify abnormal transactions. Thus, when auditors miss potential problems, the related transactions are not likely to be identified. This paper uses unsupervised outlier detection methods, which require no prior knowledge about outliers in a dataset, to identify outliers in GL data and tests whether auditors can gain new insights from those identified outliers. A framework called the Multilevel Outlier Detection Framework (MODF) is proposed to identify outliers at the transaction level, account level, and combination-by-variable level. Experiments with one real and one synthetic GL dataset demonstrate that the MODF can help auditors to gain new insights about GL data. Data Availability: The real dataset used in the experiment is not publicly available due to privacy policies. JEL Classifications: M410, M42.

Citation

Wei, D., Cho, S., Vasarhelyi, M. A., & Liam, T.-W. (2024). Outlier Detection in Auditing: Integrating Unsupervised Learning within a Multilevel Framework for General Ledger Analysis. Journal of Information Systems, 38(2), 123-142. https://doi.org/10.2308/isys-2022-026

• Journal Article Type: Article
• Acceptance Date: Apr 1, 2024
• Online Publication Date: Jun 14, 2024
• Publication Date: Jul 1, 2024
• Deposit Date: Sep 23, 2024
• Publicly Available Date: Sep 25, 2024
• Journal: Journal of Information Systems
• Peer Reviewed: Peer Reviewed
• Volume: 38
• Issue: 2
• Pages: 123-142
• DOI: https://doi.org/10.2308/isys-2022-026
• Keywords: outlier detection; auditing; data analytics; machine learning; unsupervised learning; general ledgers
• Public URL: https://durham-repository.worktribe.com/output/2874137