Options
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks
Type
conference paper
Date Issued
2021-11-03
Author(s)
Research Team
AIML Lab
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.
Language
English
Keywords
representation learning
self-supervised learning
multi-task learning
audit
computer assisted audit techniques
accounting information systems
enterprise resource planning systems
HSG Classification
contribution to scientific community
HSG Profile Area
None
Refereed
Yes
Book title
Proceedings of the Second ACM International Conference on AI in Finance
Publisher
Association for Computing Machinery (ACM)
Start page
1
End page
8
Pages
8
Event Title
ACM International Conference on Artificial Intelligence in Finance (ICAIF)
Event Location
London, United Kingdom
Event Date
November 3-5, 2021
Official URL
Subject(s)
Division(s)
Contact Email Address
marco.schreyer@unisg.ch
Eprints ID
264493
File(s)