Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
Type
conference paper
Date Issued
2022-02-28
Author(s)
Research Team
AIML Lab
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.
Language
English
HSG Classification
contribution to scientific community
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Event Title
AAAI 2022 Workshop on AI in Financial Services: Adaptiveness, Resilience & Governance
Event Location
Virtual
Event Date
February 28th, 2022
Official URL
Subject(s)
Division(s)
Contact Email Address
marco.schreyer@unisg.ch
Eprints ID
265876
File(s)![Thumbnail Image]()
Name
AAAI_2022_preprint.pdf
Size
5.69 MB
Format
Adobe PDF
Checksum (MD5)
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