Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Neural Networks

Item Type Conference or Workshop Item (Paper)
Abstract Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. 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. To overcome this disadvantage and inspired by the recent success of deep learning, we propose the application of deep autoencoder neural networks to detect anomalous journal entries. We demonstrate that the trained network's reconstruction error obtainable for a journal entry and regularized by the entry's individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment. Experiments on two real-world datasets of journal entries show the effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A) and 16.95 (dataset B) and less false positive alerts compared to state of the art baseline methods. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.
Authors Schreyer, Marco; Sattarov, Timur; Borth, Damian; Dengel, Andreas & Reimer, Bernd
Research Team AIML Lab
Language English
Keywords deep learning, machine learning, artificial intelligence, accounting
Subjects computer science
finance
Date 1 March 2018
Event Title GPU Technology Conference - Silicon Valley
Event Location San Jose, CA, USA
Event Dates March 26th - 29th, 2018
Contact Email Address marco.schreyer@unisg.ch
Additional Information Code available at: https://github.com/GitiHubi/deepAI
Depositing User Marco Schreyer
Date Deposited 14 Oct 2019 12:54
Last Modified 20 Jul 2022 17:39
URI: https://www.alexandria.unisg.ch/publications/258111

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Schreyer, Marco; Sattarov, Timur; Borth, Damian; Dengel, Andreas & Reimer, Bernd: Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Neural Networks. 2018. - GPU Technology Conference - Silicon Valley. - San Jose, CA, USA.

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https://www.alexandria.unisg.ch/id/eprint/258111
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