Anomaly Detection in General Ledger Data: Results from a Hybrid Approach

Abstract:Journal Entry Tests (JETs) are a mandatory part of annual audits to evaluate and assess both highrisk 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.

Machine Learning (cs.LG)

arXiv:2609.18228 [cs.LG]

(or arXiv:2609.18228v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.18228 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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