Deep autoencoder neural networks can be used to detect anomalous journal entries. Experiments on two real-world datasets of journal entries show the effectiveness of the approach. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.
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https://github.com/GitiHubi/deepAD
https://arxiv.org/pdf/1709.05254.pdf