Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
This work addresses the problem of real-time anomaly detection in financial auditing for auditors, but it appears incremental as it combines existing federated and continual learning strategies.
The authors tackled the challenge of learning adaptive audit models in decentralized and dynamic financial auditing settings by proposing a Federated Continual Learning framework, which demonstrated the model's ability to detect accounting anomalies in scenarios with data distribution shifts.
The International Standards on Auditing 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 decentralised 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.