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

Author

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  • Marco Schreyer
  • Timur Sattarov
  • Christian Schulze
  • Bernd Reimer
  • Damian Borth

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.

Suggested Citation

  • Marco Schreyer & Timur Sattarov & Christian Schulze & Bernd Reimer & Damian Borth, 2019. "Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks," Papers 1908.00734, arXiv.org.
  • Handle: RePEc:arx:papers:1908.00734
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    File URL: http://arxiv.org/pdf/1908.00734
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    References listed on IDEAS

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    1. Jans, Mieke & Lybaert, Nadine & Vanhoof, Koen, 2010. "Internal fraud risk reduction: Results of a data mining case study," International Journal of Accounting Information Systems, Elsevier, vol. 11(1), pages 17-41.
    2. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    3. Debreceny, Roger S. & Gray, Glen L., 2010. "Data mining journal entries for fraud detection: An exploratory study," International Journal of Accounting Information Systems, Elsevier, vol. 11(3), pages 157-181.
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    Cited by:

    1. Ricardo Muller & Marco Schreyer & Timur Sattarov & Damian Borth, 2022. "RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations," Papers 2209.09157, arXiv.org.
    2. Ahmad Faisal Hayek & Nora Azima Noordin & Khaled Hussainey, 2022. "Machine Learning and External Auditor Perception: An Analysis for UAE External Auditors Using Technology Acceptance Model," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 21(4), pages 475-500, December.

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