Adversarial Learning of Deepfakes in Accounting

Item Type Conference or Workshop Item (Paper)
Abstract Nowadays, organizations collect vast quantities of accounting relevant transactions, referred to as 'journal entries', in 'Enterprise Resource Planning' (ERP) systems. The aggregation of those entries ultimately defines an organization's financial statement. To detect potential misstatements and fraud, international audit standards demand auditors to directly assess journal entries using 'Computer Assisted AuditTechniques' (CAATs). At the same time, discoveries in deep learning research revealed that machine learning models are vulnerable to 'adversarial attacks'. It also became evident that such attack techniques can be misused to generate 'Deepfakes' designed to directly attack the perception of humans by creating convincingly altered media content. The research of such developments and their potential impact on the finance and accounting domain is still in its early stage. We believe that it is of vital relevance to investigate how such techniques could be maliciously misused in this sphere. In this work, we show an adversarial attack against CAATs using deep neural networks. We first introduce a real-world 'thread model' designed to camouflage accounting anomalies such as fraudulent journal entries. Second, we show that adversarial autoencoder neural networks are capable of learning a human interpretable model of journal entries that disentangles the entries latent generative factors. Finally, we demonstrate how such a model can be maliciously misused by a perpetrator to generate robust 'adversarial' journal entries that mislead CAATs.
Authors Schreyer, Marco; Sattarov, Timur; Reimer, Bernd & Borth, Damian
Language English
Keywords deep learning, accounting, machine learning, artificial intelligence
Subjects computer science
finance
HSG Classification contribution to scientific community
HSG Profile Area None
Date 13 December 2019
Number of Pages 16
Event Title NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy
Event Location Vancouver, British Columbia, Canada
Event Dates December 13, 2019
Contact Email Address marco.schreyer@unisg.ch
Depositing User Marco Schreyer
Date Deposited 10 Oct 2019 11:58
Last Modified 17 Aug 2020 13:03
URI: https://www.alexandria.unisg.ch/publications/258090

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Schreyer, Marco; Sattarov, Timur; Reimer, Bernd & Borth, Damian: Adversarial Learning of Deepfakes in Accounting. 2019. - NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy. - Vancouver, British Columbia, Canada.

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https://www.alexandria.unisg.ch/id/eprint/258090
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