mlsanityData to 14 Oct 2026

Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing

arXiv 2210.15051 ↗First posted 26 Oct 2022cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 37 of its 86 references that are in the map. First posted on arXiv 26 Oct 2022.

Builds on

37 of 86 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  2. 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
  3. 2024Federated Continual Learning for Edge-AI: A Comprehensive Surveycited by 11
  4. 2024FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generationcited by 7