Publikation
Anomaly Detection in General Ledger Data: Results from a Hybrid Approach
Jan Gronewald; Alexander Michael Rombach; Sebastian Stephan; Peter Fettke
1st International Conference on Auditing and Artificial Intelligence, 2024.
Zusammenfassung
Journal Entry Tests (JETs) are a mandatory part of annual audits to evaluate and assess both high
risk audit areas and potential material misstatements. However, as JETs are designed to detect
known patterns based on domain knowledge, the resulting lists are often very large and require
substantial additional effort from the auditor. To ensure the economic efficiency of the audit, the
number of false positives in JET result lists must be reduced. Especially machine learning (ML)
methods represent a promising approach to improve anomaly detection in this field. In this research
in progress paper, we investigate different approaches on how to combine JETs with ML-methods
in a hybrid manner. We present specialized models to increase the detection performance and
validity of anomaly detection results to improve audit efficiency. The experiments are based on
synthetic data consisting of different normal and anomalous journal entries.