Identifying online credit card fraud using artificial immune systems

DC FieldValueLanguage
dc.contributor.authorBrabazon, Anthony-
dc.contributor.authorCahill, Jane-
dc.contributor.authorKeenan, Peter-
dc.contributor.authorWalsh, Daniel-
dc.date.accessioned2011-01-20T16:46:33Z-
dc.date.available2011-01-20T16:46:33Z-
dc.date.copyright2010 IEEEen
dc.date.issued2010-07-
dc.identifier.isbn978-1-4244-6909-3-
dc.identifier.urihttp://hdl.handle.net/10197/2736-
dc.descriptionCongress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 Julyen
dc.description.abstractSignificant payment flows now take place on-line, giving rise to a requirement for efficient and effective systems for the detection of credit card fraud. A particular aspect of this problem is that it is highly dynamic, as fraudsters continually adapt their strategies in response to the increasing sophistication of detection systems. Hence, system training by exposure to examples of previous examples of fraudulent transactions can lead to fraud detection systems which are susceptible to new patterns of fraudulent transactions. The nature of the problem suggests that Artificial Immune Systems (AIS) may have particular utility for inclusion in fraud detection systems as AIS can be constructed which can flag ‘non standard’ transactions without having seen examples of all possible such transactions during training of the algorithm. In this paper, we investigate the effectiveness of Artificial Immune Systems (AIS) for credit card fraud detection using a large dataset obtained from an on-line retailer. Three AIS algorithms were implemented and their performance was benchmarked against a logistic regression model. The results suggest that AIS algorithms have potential for inclusion in fraud detection systems but that further work is required to realize their full potential in this domain.en
dc.description.sponsorshipScience Foundation Irelanden
dc.description.uriConference detailsen
dc.description.urihttp://www.wcci2010.org/en
dc.format.extent191694 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.publisherIEEE Pressen
dc.relation.ispartof2010 IEEE Congress on Evolutionary Computation (CEC) [proceedings]en
dc.relation.requiresFMC² Research Collectionen
dc.rightsPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectCredit card frauden
dc.subjectArtificial immune systemsen
dc.subject.lcshCredit card frauden
dc.subject.lcshImmunocomputersen
dc.titleIdentifying online credit card fraud using artificial immune systemsen
dc.typeConference Publicationen
dc.internal.availabilityFull text availableen
dc.internal.webversionshttp://dx.doi.org/10.1109/CEC.2010.5586154-
dc.statusPeer revieweden
dc.identifier.doi10.1109/CEC.2010.5586154-
dc.neeo.contributorBrabazon|Anthony|aut|-
dc.neeo.contributorCahill|Jane|aut|-
dc.neeo.contributorKeenan|Peter|aut|-
dc.neeo.contributorWalsh|Daniel|aut|-
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:FMC² Research Collection
Computer Science Research Collection
Business Research Collection
CASL Research Collection
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