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

Journal
Expert Focus
Type
journal article
Date Issued
2022-03-07
Author(s)
Schreyer, Marco  
Gierbl, Anita Stefanie  
Ruud, Flemming  
Borth, Damian  orcid-logo
Research Team
AIML Lab
Abstract (De)
Artificial Intelligence (AI) is increasingly perceived as a valuable technology in internal and external auditing. The following article introduces the application of deep learning (DL), a thriving subdiscipline of AI, to draw a learning-based representative audit sample from extensive volumes of financial accounting data.
Language
English
Keywords
Artificial Intelligence
Deep Learning
Auditing
Audit Sampling
Journal Entry Testing
Accounting
HSG Classification
contribution to practical use / society
HSG Profile Area
None
Refereed
Yes
Publisher
EXPERTsuisse
Publisher place
Initial Publication in EXPERT FOCUS 2022/April
Number
04
Start page
106
End page
112
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/108891
Subject(s)

computer science

business studies

Division(s)

ICS - Institute of Co...

ACA - Institute of Ac...

Contact Email Address
marco.schreyer@unisg.ch
Eprints ID
267550
File(s)
Loading...
Thumbnail Image

open.access

Name

2022_ExpertFocus_Schreyer_Gierbl_Ruud_Borth_EN.pdf

Size

541.1 KB

Format

Adobe PDF

Checksum (MD5)

407c132f9c9e748e7033229017aef460

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