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.Comment: 6 pages (excl. appendix), 5 figures, 1 table, preprint version,
currently under revie