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 |