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On an ant colony-based approach for business fraud detection

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dc.contributor.authorLiu, Ou-
dc.contributor.authorMa, Jian-
dc.contributor.authorPoon, Pak-Lok-
dc.contributor.authorZhang, Jun-
dc.date.accessioned2024-01-20T09:01:44Z-
dc.date.available2024-01-20T09:01:44Z-
dc.date.issued2009-09-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/117805-
dc.description.abstractNowadays we witness an increasing number of business frauds. To protect investors' interest, a financial firm should possess an effective means to detect such frauds. In this regard, artificial neural networks (ANNs) are widely used for fraud detection. Traditional back-propagation-based algorithms used for training an ANN, however, exhibit the local optima problem, thus reducing the effectiveness of an ANN in detecting frauds. To alleviate the problem, this paper proposes an approach to training an ANN using an ant colony optimization technique, through which the local optima problem can be solved and the effectiveness of an ANN in fraud detection can be improved. Based on our approach, an associated prototype system is designed and implemented, and an exploratory study is performed. The results of the study are encouraging, showing the viability of our proposed approach. © 2009 Springer Berlin Heidelberg.-
dc.format.extent8-
dc.language영어-
dc.language.isoENG-
dc.publisherSpringer Verlag-
dc.titleOn an ant colony-based approach for business fraud detection-
dc.typeArticle-
dc.publisher.location독일-
dc.identifier.doi10.1007/978-3-642-04070-2_116-
dc.identifier.scopusid2-s2.0-70350405424-
dc.identifier.wosid000271604900116-
dc.identifier.bibliographicCitationEmerging Intelligent Computing Technology and Applications 5th International Conference on Intelligent Computing, ICIC 2009 Ulsan, South Korea, September 16-19, 2009 Proceedings, v.5754 , pp 1104 - 1111-
dc.citation.titleEmerging Intelligent Computing Technology and Applications 5th International Conference on Intelligent Computing, ICIC 2009 Ulsan, South Korea, September 16-19, 2009 Proceedings-
dc.citation.volume5754-
dc.citation.startPage1104-
dc.citation.endPage1111-
dc.type.docTypeConference paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusPREVENTION-
dc.subject.keywordPlusMODEL-
dc.subject.keywordAuthorAnt colony optimization-
dc.subject.keywordAuthorArtificial neural network-
dc.subject.keywordAuthorFraud detection-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-642-04070-2_116-
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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