Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
Type
conference paper
Date Issued
2022-12-02
Author(s)
Research Team
AIML Lab
Abstract
The International Standards on Auditing (ISA) require auditors to collect reasonable assurance that financial statements are free of material misstatement. At the same time, a central objective of Continuous Assurance is the real-time assessment of digital accounting journal entries. Recently, driven by the advances in artificial intelligence, Deep Learning techniques have emerged in financial auditing to examine vast quantities of accounting data. However, learning highly adaptive audit models in decentralized and dynamic settings remains challenging. It requires the study of data distribution shifts over multiple clients and time periods. In this work, we propose a Federated Continual Learning framework enabling auditors to learn audit models from decentral clients continuously. We evaluate the framework's ability to detect accounting anomalies in common scenarios of organizational activity. Our empirical results, using real-world datasets and combined federated-continual learning strategies, demonstrate the learned model's ability to detect anomalies in audit settings of data distribution shifts.
Language
English
Keywords
artificial intelligence
federated learning
continual learning
financial auditing
anomaly detection
HSG Classification
contribution to scientific community
HSG Profile Area
None
Event Title
Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
Event Location
New Orleans, LA, USA
Event Date
Mon Nov 28th - Dec 9th
Subject(s)
Division(s)
Contact Email Address
marco.schreyer@unisg.ch
Additional Information
International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 (FL-NeurIPS'22)
Eprints ID
268236
File(s)![Thumbnail Image]()
open.access
Name
NeurIPS_2022_final.pdf
Size
5.02 MB
Format
Adobe PDF
Checksum (MD5)
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