Abstract
Process mining is an efficient method that can analyze the full population of transactions using the event log of business processes. Conventional rule-based process mining techniques can detect anomalies; however, it tends to trigger a large number of false alarms. To improve the efficiency of anomaly detection using process mining, this study adopts a deep learning-based classification approach to detect anomalies in the traces of event logs. This approach contributes to the literature by proposing a non-rule-based process mining technique based on deep learning. Results demonstrate that the proposed non-rule-based process mining method can help auditors focus on transactional anomalies.
| Original language | American English |
|---|---|
| Pages (from-to) | 119-140 |
| Number of pages | 22 |
| Journal | International Journal of Digital Accounting Research |
| Volume | 24 |
| DOIs | |
| State | Published - Nov 1 2024 |
ASJC Scopus subject areas
- Accounting
- Finance
- Information Systems and Management
Keywords
- anomaly detection
- deep learning
- fraudulent activities
- Process mining