Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks

Item Type	Conference or Workshop Item (Paper)
Abstract	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.
Authors	Schreyer, Marco; Sattarov, Timur; Schulze, Christian; Reimer, Bernd & Borth, Damian
Research Team	AIML Lab
Language	English
Keywords	deep learning, accounting, artificial intelligence, machine learning
Subjects	computer science
HSG Classification	contribution to scientific community
Date	5 August 2019
Event Title	2nd KDD Workshop on Anomaly Detection in Finance, 2019
Event Location	Anchorage, Alaska, USA
Event Dates	August 05, 2019
Official URL	https://arxiv.org/abs/1908.00734
Contact Email Address	Marco.Schreyer@unisg.ch
Additional Information	Code available at: https://github.com/GitiHubi/deepAD
Depositing User	Marco Schreyer
Date Deposited	02 Sep 2019 14:49
Last Modified	20 Jul 2022 17:39
URI:	https://www.alexandria.unisg.ch/publications/257633

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Schreyer, Marco; Sattarov, Timur; Schulze, Christian; Reimer, Bernd & Borth, Damian: Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks. 2019. - 2nd KDD Workshop on Anomaly Detection in Finance, 2019. - Anchorage, Alaska, USA.

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https://www.alexandria.unisg.ch/id/eprint/257633

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