Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
This addresses the challenge of adapting deep-learning models to evolving data streams in financial auditing, which is an incremental improvement for domain-specific applications.
The paper tackles the problem of distribution changes and knowledge interference in continuous auditing of financial data by proposing a continual anomaly detection framework, with experimental results showing it can reduce false-positive alerts and false-negative decisions.
International audit standards require the direct assessment of a financial statement's underlying accounting journal entries. Driven by advances in artificial intelligence, deep-learning inspired audit techniques emerged to examine vast quantities of journal entry data. However, in regular audits, most of the proposed methods are applied to learn from a comparably stationary journal entry population, e.g., of a financial quarter or year. Ignoring situations where audit relevant distribution changes are not evident in the training data or become incrementally available over time. In contrast, in continuous auditing, deep-learning models are continually trained on a stream of recorded journal entries, e.g., of the last hour. Resulting in situations where previous knowledge interferes with new information and will be entirely overwritten. This work proposes a continual anomaly detection framework to overcome both challenges and designed to learn from a stream of journal entry data experiences. The framework is evaluated based on deliberately designed audit scenarios and two real-world datasets. Our experimental results provide initial evidence that such a learning scheme offers the ability to reduce false-positive alerts and false-negative decisions.