  The detection of fraud in accounting data is a long-standing challenge in
financial statement audits. Nowadays, the majority of applied techniques refer
to handcrafted rules derived from known fraud scenarios. While fairly
successful, these rules exhibit the drawback that they often fail to generalize
beyond known fraud scenarios and fraudsters gradually find ways to circumvent
them. In contrast, more advanced approaches inspired by the recent success of
deep learning often lack seamless interpretability of the detected results. To
overcome this challenge, we propose the application of adversarial autoencoder
networks. We demonstrate that such artificial neural networks are capable of
learning a semantic meaningful representation of real-world journal entries.
The learned representation provides a holistic view on a given set of journal
entries and significantly improves the interpretability of detected accounting
anomalies. We show that such a representation combined with the networks
reconstruction error can be utilized as an unsupervised and highly adaptive
anomaly assessment. Experiments on two datasets and initial feedback received
by forensic accountants underpinned the effectiveness of the approach.
