英文标题：
《Detection of Accounting Anomalies in the Latent Space using Adversarial
  Autoencoder Neural Networks》
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作者：
Marco Schreyer, Timur Sattarov, Christian Schulze, Bernd Reimer, and
  Damian Borth
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最新提交年份：
2019
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英文摘要：
  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.
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中文摘要：
会计数据欺诈的检测是财务报表审计中的一个长期挑战。如今，大多数应用的技术都是指从已知欺诈场景中衍生出来的手工规则。这些规则虽然相当成功，但也存在一个缺点，即它们往往无法推广到已知的欺诈场景之外，欺诈者逐渐找到规避这些规则的方法。相比之下，受最近深度学习成功启发的更先进方法往往缺乏对检测结果的无缝解释。为了克服这一挑战，我们提出了对抗式自动编码器网络的应用。我们证明了这种人工神经网络能够学习真实世界日记条目的语义有意义表示。所学的表示法提供了一组给定日记账分录的整体视图，并显著提高了检测到的会计异常的可解释性。我们表明，这种表示结合网络重构误差可以作为一种无监督的、高度自适应的异常评估。在两个数据集上的实验和法务会计师收到的初步反馈支持了该方法的有效性。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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