Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data

• URL: http://arxiv.org/abs/2112.13215v1
• Date: Sat, 25 Dec 2021 09:21:14 GMT
• Title: Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
• Authors: Hamed Hemati, Marco Schreyer, Damian Borth
• Abstract summary: International audit standards require the direct assessment of a financial statement's underlying accounting journal entries. Deep-learning inspired audit techniques emerged to examine vast quantities of journal entry data. This work proposes a continual anomaly detection framework to overcome both challenges and designed to learn from a stream of journal entry data experiences.
• Score: 1.9659095632676094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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