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.

本研究提出了使用对抗自编码网络来检测会计数据中的欺诈行为，该方法可以学习到现实世界会计分录的语义表示，从而提高了检测方法的可解释性和准确性。实验证明该方法的有效性。

使用对抗自编码神经网络探测潜空间中的会计异常

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

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.

本研究使用深度自编码神经网络进行高效的会计异常检测，实验结果表明，此方法在比基准方法下有更少的误报率且能捕捉高度相关的会计异常。