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  4. Learning Sampling in Financial Statement Audits using Vector Quantised Variational Autoencoder Neural Networks
 
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Learning Sampling in Financial Statement Audits using Vector Quantised Variational Autoencoder Neural Networks

Journal
Proceedings of the First ACM International Conference on AI in Finance
Type
conference paper
Date Issued
2020-10-01
Author(s)
Schreyer, Marco  
Timur Sattarov
Gierbl, Anita Stefanie  
Reimer, Bernd
Borth, Damian  orcid-logo
Editor(s)
Marco Schreyer  
DOI
10.1145/3383455.3422546
Research Team
AIML Lab
Abstract
The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement ('true and fair presentation'). International audit standards require the assessment of a statements' underlying accounting relevant transactions referred to as 'journal entries' to detect potential misstatements. To efficiently audit the increasing quantities of such journal entries, auditors regularly conduct an 'audit sampling' i.e. a sample-based assessment of a subset of these journal entries. However, the task of audit sampling is often conducted early in the overall audit process, where the auditor might not be aware of all generative factors and their dynamics that resulted in the journal entries in-scope of the audit. To overcome this challenge, we propose the use of a Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks to learn a representation of journal entries able to provide a comprehensive 'audit sampling' to the auditor. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data. We show that the learned quantisation uncovers (i) the latent factors of variation and (ii) can be utilised as a highly representative audit sample in financial statement audits.
Language
English
Keywords
Machine Learning
Deep Learning
Auditing
Accounting
Enterprise Resource Planning
HSG Classification
contribution to scientific community
HSG Profile Area
None
Publisher
Association of Computing Machinery (ACM)
Start page
1
End page
8
Pages
8
Event Title
ICAIF '20: First ACM International Conference on AI in Finance
Event Location
New York, NY, USA
Event Date
15-16 October, 2020
Official URL
https://dl.acm.org/doi/10.1145/3383455.3422546
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/113091
Subject(s)

computer science

finance

Division(s)

SCS - School of Compu...

ACA - Institute of Ac...

Contact Email Address
marco.schreyer@unisg.ch
Eprints ID
260768
File(s)
Loading...
Thumbnail Image

open.access

Name

2008.02528.pdf

Size

5.42 MB

Format

Adobe PDF

Checksum (MD5)

d59d8063404ee006f9ef20166984a2fe

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