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.
| Original language | American English |
|---|---|
| Pages (from-to) | 123-142 |
| Number of pages | 20 |
| Journal | Journal of Information Systems |
| Volume | 38 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2024 |
ASJC Scopus subject areas
- Management Information Systems
- Software
- Information Systems
- Accounting
- Human-Computer Interaction
- Information Systems and Management
- Management of Technology and Innovation
Keywords
- auditing
- data analytics
- general ledgers
- machine learning
- outlier detection
- unsupervised learning