Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits.

Schultz, Martin; Tropmann-Frick, Marina

doi:10.24251/HICSS.2020.666

DC Element	Wert	Sprache
dc.contributor.author	Schultz, Martin	-
dc.contributor.author	Tropmann-Frick, Marina	-
dc.date.accessioned	2020-09-02T15:37:19Z	-
dc.date.available	2020-09-02T15:37:19Z	-
dc.date.issued	2020-01	-
dc.identifier.isbn	978-0-9981331-3-3	en_US
dc.identifier.uri	http://hdl.handle.net/20.500.12738/4320	-
dc.title	Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits.
dc.type	inProceedings	en_US
tuhh.oai.show	true	en_US
tuhh.publication.institute	Department Informatik	en_US
tuhh.publication.institute	Fakultät Design, Medien und Information	en_US
tuhh.publisher.doi	10.24251/HICSS.2020.666	-
tuhh.type.opus	InProceedings (Aufsatz / Paper einer Konferenz etc.)	-
dc.type.casrai	Conference Paper	-
dc.type.dini	contributionToPeriodical	-
dc.type.driver	contributionToPeriodical	-
dcterms.DCMIType	Text	-
item.fulltext	No Fulltext	-
item.creatorGND	Schultz, Martin	-
item.creatorGND	Tropmann-Frick, Marina	-
item.openairetype	inProceedings	-
item.grantfulltext	none	-
item.creatorOrcid	Schultz, Martin	-
item.creatorOrcid	Tropmann-Frick, Marina	-
item.cerifentitytype	Publications	-
item.openairecristype	http://purl.org/coar/resource_type/c_5794	-
crisitem.author.dept	Department Informatik	-
crisitem.author.dept	Department Informatik	-
crisitem.author.orcid	0009-0009-7920-3823	-
crisitem.author.orcid	0000-0003-1623-5309	-
crisitem.author.parentorg	Fakultät Technik und Informatik	-
crisitem.author.parentorg	Fakultät Technik und Informatik	-
Enthalten in den Sammlungen:	Publications without full text