Adversarial Learning of Deepfakes in Accounting
Type
conference paper
Date Issued
2019-12-13
Author(s)
Research Team
AIML Lab
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.
Language
English
Keywords
deep learning
accounting
machine learning
artificial intelligence
HSG Classification
contribution to scientific community
HSG Profile Area
None
Refereed
Yes
Publisher
Cornell University - arXiv
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 Date
December 13, 2019
Official URL
Subject(s)
Division(s)
Contact Email Address
marco.schreyer@unisg.ch
Eprints ID
258090
File(s)![Thumbnail Image]()
Name
1910.03810.pdf
Size
6.91 MB
Format
Adobe PDF
Checksum (MD5)
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