Marco Schreyer

Marco Schreyer

Last Name

Schreyer

First name

Marco

Email

marco.schreyer@unisg.ch

ORCID

0009-0001-3800-2226

Phone

+41 71 224 79 13

Homepage

https://gitihubi.github.io

Google Scholar

https://scholar.google.com/citations?user=O6V5YkEAAAAJ

Now showing 1 - 5 of 5

Artificial Intelligence Enabled Audit Sampling - Learning to draw representative and interpretable audit samples from large-scale journal entry data

(EXPERTsuisse, 2022-03-07)
Schreyer, Marco
;
Gierbl, Anita Stefanie
;
Ruud, Flemming
;
Borth, Damian

Type: journal article

Journal: Expert Focus

Issue: 04

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108891
Stichprobenauswahl durch die Anwendung von Künstlicher Intelligenz - Lernen repräsentativer Stichproben aus Journalbuchungen in der Prüfungspraxis

(EXPERTsuisse, 2022-02-07)
Schreyer, Marco
;
Gierbl, Anita Stefanie
;
Ruud, Flemming
;
Borth, Damian

Type: journal article

Journal: Expert Focus

Issue: 02

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/109010
Deep Learning für die Wirtschaftsprüfung - Eine Darstellung von Theorie, Funktionsweise und Anwendungsmöglichkeiten

(C.H. Beck Vahlen Verlag, 2021-07-28)
Gierbl, Anita Stefanie
;
Schreyer, Marco
;
Borth, Damian
;
Leibfried, Peter

Type: journal article

Journal: Zeitschrift für Internationale Rechnungslegung (IRZ)

Issue: 7/8

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/110189
Künstliche Intelligenz in der Prüfungspraxis - Eine Bestandsaufnahme aktueller Einsatzmöglichkeiten und Herausforderungen

(Expertsuisse, 2020-09-01)
Gierbl, Anita Stefanie
;
Schreyer, Marco
;
Leibfried, Peter
;
Borth, Damian

Type: journal article

Journal: Expert Focus

Volume: 2020

Issue: 09

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/111804
Learning Sampling in Financial Statement Audits using Vector Quantised Variational Autoencoder Neural Networks

(Association of Computing Machinery (ACM), 2020-10-01)
Schreyer, Marco
;
Timur Sattarov
;
Gierbl, Anita Stefanie
;
Reimer, Bernd
;
Borth, Damian
;
Marco Schreyer

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.

Type: conference paper

Journal: Proceedings of the First ACM International Conference on AI in Finance

URL: https://dl.acm.org/doi/10.1145/3383455.3422546

DOI: 10.1145/3383455.3422546

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/113091

Scopus© Citations 4

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