论文标题
联合持续学习以检测财务审计中的会计异常
Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
论文作者
论文摘要
国际审计标准要求审核员收集合理的保证财务报表没有物质陈述。同时，连续保证的核心目标是对数字会计期刊条目的实时评估。最近，在人工智能的进步驱动下，财务审核中已经出现了深度学习技术，以检查大量会计数据。但是，在分散和动态设置中学习高度适应性的审计模型仍然具有挑战性。它需要研究数据分布在多个客户和时间段上的变化。在这项工作中，我们提出了一个联合的持续学习框架，使审计师能够不断地从分散客户那里学习审计模型。我们评估了该框架在组织活动的常见情况下检测会计异常的能力。我们使用现实世界数据集并将联合持续学习策略结合在一起的经验结果证明了学识渊博的模型在数据分配变化的审计设置中检测异常的能力。
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.
