| Item Type |
Conference or Workshop Item
(Paper)
|
| Abstract |
The ongoing 'digital transformation' fundamentally changes audit evidence's nature, recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires auditors to examine vast volumes of a financial statement's underlying digital accounting records. As a result, audit firms also `digitize' their analytical capabilities and invest in Deep Learning (DL), a successful sub-discipline of Machine Learning. The application of DL offers the ability to learn specialized audit models from data of multiple clients, e.g., organizations operating in the same industry or jurisdiction. In general, regulations require auditors to adhere to strict data confidentiality measures. At the same time, recent intriguing discoveries showed that large-scale DL models are vulnerable to leaking sensitive training data information. Today, it often remains unclear how audit firms can apply DL models while complying with data protection regulations. In this work, we propose a Federated Learning framework to train DL models on auditing relevant accounting data of multiple clients. The framework encompasses Differential Privacy and Split Learning capabilities to mitigate data confidentiality risks at model inference. Our results provide empirical evidence that auditors can benefit from DL models that accumulate knowledge from multiple sources of proprietary client data. |
| Authors |
Schreyer, Marco; Sattarov, Timur & Borth, Damian |
| Research Team |
AIML Lab |
| Language |
English |
| Keywords |
federated learning, differential privacy, financial auditing, anomaly detection, computer-assisted audit techniques, accounting information systems, enterprise resource planning systems |
| Subjects |
computer science |
| HSG Classification |
contribution to scientific community |
| HSG Profile Area |
None |
| Date |
1 November 2022 |
| Title of Book |
Proceedings of the ACM International Conference on Artificial Intelligence in Finance (ICAIF) |
| Event Title |
3rd ACM International Conference on Artificial Intelligence in Finance (ICAIF) |
| Event Location |
New York City, USA |
| Event Dates |
Nov. 2-4, 2022 |
| Contact Email Address |
marco.schreyer@unisg.ch |
| Depositing User |
Marco Schreyer
|
| Date Deposited |
02 Sep 2022 16:54 |
| Last Modified |
28 Sep 2022 09:19 |
| URI: |
https://www.alexandria.unisg.ch/publications/267136 |
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits
![[img]](http://www.alexandria.unisg.ch/style/images/fileicons/oa_orange.png)
