Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks

Item Type Conference or Workshop Item (Paper)
Abstract International audit standards require the direct assessment of a financial statement’s underlying accounting transactions, referred to as journal entries. Recently, driven by the advances in artificial intelligence, deep learning inspired audit techniques have emerged in the field of auditing vast quantities of journal entry data. Nowadays, the majority of such methods rely on a set of specialized models, each trained for a particular audit task. At the same time, when conducting a financial statement audit, audit teams are confronted with (i) challenging time-budget constraints, (ii) extensive documentation obligations, and (iii) strict model interpretability requirements. As a result, auditors prefer to harness only a single preferably ‘multi-purpose’ model throughout an audit engagement. We propose a contrastive self-supervised learning framework designed to learn audit task invariant accounting data representations to meet this requirement. The framework encompasses deliberate interacting data augmentation policies that utilize the attribute characteristics of journal entry data. We evaluate the framework on two real-world datasets of city payments and transfer the learned representations to three downstream audit tasks: anomaly detection, audit sampling, and audit documentation. Our experimental results provide empirical evidence that the proposed framework offers the ability to increase the efficiency of audits by learning rich and interpretable ‘multi-task’ representations.
Authors Schreyer, Marco; Sattarov, Timur & Borth, Damian
Research Team AIML Lab
Language English
Keywords representation learning, self-supervised learning, multi-task learning, audit, computer assisted audit techniques, accounting information systems, enterprise resource planning systems
Subjects computer science
finance
HSG Classification contribution to scientific community
HSG Profile Area None
Date 3 November 2021
Publisher Association for Computing Machinery (ACM)
Number of Pages 8
Title of Book Proceedings of the ACM International Conference on Artificial Intelligence in Finance (ICAIF)
Event Title ACM International Conference on Artificial Intelligence in Finance (ICAIF)
Event Location London, United Kingdom
Event Dates November 3-5, 2021
Contact Email Address marco.schreyer@unisg.ch
Depositing User Marco Schreyer
Date Deposited 04 Oct 2021 11:46
Last Modified 20 Jul 2022 17:46
URI: https://www.alexandria.unisg.ch/publications/264493

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Schreyer, Marco; Sattarov, Timur & Borth, Damian: Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks. 2021. - ACM International Conference on Artificial Intelligence in Finance (ICAIF). - London, United Kingdom.

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https://www.alexandria.unisg.ch/id/eprint/264493
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