Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing

Item Type Conference or Workshop Item (Paper)
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.
Authors Schreyer, Marco; Hemati, Hamed; Borth, Damian & Vasarhelyi, Miklos A.
Research Team AIML Lab
Language English
Keywords artificial intelligence, federated learning, continual learning, financial auditing, anomaly detection
Subjects business studies
computer science
HSG Classification contribution to scientific community
HSG Profile Area None
Date 2 December 2022
Event Title Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
Event Location New Orleans, LA, USA
Event Dates Mon Nov 28th - Dec 9th
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)
Depositing User Marco Schreyer
Date Deposited 03 Dec 2022 10:42
Last Modified 03 Dec 2022 10:42
URI: https://www.alexandria.unisg.ch/publications/268236

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Schreyer, Marco; Hemati, Hamed; Borth, Damian & Vasarhelyi, Miklos A.: Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing. 2022. - Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022). - New Orleans, LA, USA.

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https://www.alexandria.unisg.ch/id/eprint/268236
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