A Comprehensive Survey of Fraud Detection for E-Commerce

Alharbi, S; Alahmadi, K; Song, Z; Wang, X

A Comprehensive Survey of Fraud Detection for E-Commerce

Alharbi, S Alahmadi, K Song, Z Wang, X

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Publisher:: ScopeMed
Publication Type:: Journal Article
Citation:: European Journal of Computer Sciences and Informatics, 2026, 3, (2), pp. 83-83
Issue Date:: 2026

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Field	Value	Language
dc.contributor.author	Alharbi, S
dc.contributor.author	Alahmadi, K
dc.contributor.author	Song, Z
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.date.accessioned	2026-04-29T07:06:53Z
dc.date.available	2026-04-29T07:06:53Z
dc.date.issued	2026
dc.identifier.citation	European Journal of Computer Sciences and Informatics, 2026, 3, (2), pp. 83-83
dc.identifier.issn	3041-4849
dc.identifier.uri	http://hdl.handle.net/10453/194833
dc.description.abstract	<jats:p>Fraud detection is crucial for maintaining the security and integrity of e-commerce operations. Recent efforts have derived fruitful results in fraud detection research, ranging from conventional machine learning to deep neural networks to more recent graph-based methods and hybrid models that combine the above techniques. This study presents a comprehensive review of common fraud types and a critical analysis of state-of-the-art fraud detection techniques in e-commerce, followed by a discussion of open challenges and future directions. This survey could be an ideal starting point for novice readers to gain a holistic understanding of the key concepts and techniques related to e-commerce fraud detection. It also serves as a reference for relevant researchers and practitioners to refresh their knowledge in the vibrant field of e-commerce fraud detection in face of evolving fraudulent threats.</jats:p>
dc.language	en
dc.publisher	ScopeMed
dc.relation.ispartof	European Journal of Computer Sciences and Informatics
dc.relation.isbasedon	10.5455/ejcsi.20250409024032
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Comprehensive Survey of Fraud Detection for E-Commerce
dc.type	Journal Article
utslib.citation.volume	3
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.date.updated	2026-04-29T07:06:52Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	3
utslib.citation.issue	2

Abstract:

Fraud detection is crucial for maintaining the security and integrity of e-commerce operations. Recent efforts have derived fruitful results in fraud detection research, ranging from conventional machine learning to deep neural networks to more recent graph-based methods and hybrid models that combine the above techniques. This study presents a comprehensive review of common fraud types and a critical analysis of state-of-the-art fraud detection techniques in e-commerce, followed by a discussion of open challenges and future directions. This survey could be an ideal starting point for novice readers to gain a holistic understanding of the key concepts and techniques related to e-commerce fraud detection. It also serves as a reference for relevant researchers and practitioners to refresh their knowledge in the vibrant field of e-commerce fraud detection in face of evolving fraudulent threats.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/194833