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Systemic acquired critique of credit card deception exposure through machine learning

2022-10Journal article Open access
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dc.contributor.authorDantas, Rui
dc.contributor.authorFirdaus, Raheela
dc.contributor.authorJaleel, Dr. Farrokh
dc.contributor.authorMata, Pedro
dc.contributor.authorMata, Mário Nuno
dc.contributor.authorLi, Gang
dc.date.accessioned2023-02-08T16:21:41Z
dc.date.available2023-02-08T16:21:41Z
dc.date.issued2022-10
dc.descriptionArtigo publicado em revista científica internacionalpt_PT
dc.description.abstractA wide range of recent studies are focusing on current issues of financial fraud, especially concerning cybercrimes. The reason behind this is even with improved security, a great amount of money loss occurs every year due to credit card fraud. In recent days, ATM fraud has decreased, while credit card fraud has increased. This study examines articles from five foremost databases. The literature review is designed using extraction by database, keywords, year, articles, authors, and performance measures based on data used in previous research, future research directions and purpose of the article. This study identifies the crucial gaps which ultimately allow research opportunities in this fraud detection process by utilizing knowledge from the machine learning domain. Our findings prove that this research area has become most dominant in the last ten years. We accessed both supervised and unsupervised machine learning techniques to detect cybercrime and management techniques which provide evidence for the effectiveness of machine learning techniques to control cybercrime in the credit card industry. Results indicated that there is room for further research to obtain better results than existing ones on the basis of both quantitative and qualitative research analysis.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationDantas, R. M., Firdaus, R., Jaleel, F., Neves Mata, P., Mata, M. N., & Li, G. (2022). Systemic Acquired Critique of Credit Card Deception Exposure through Machine Learning. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 192. https://doi.org/10.3390/joitmc8040192pt_PT
dc.identifier.doihttps://doi.org/10.3390/joitmc8040192pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/15504
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.ispartofseries;4
dc.relation.publisherversionhttps://www.mdpi.com/2199-8531/8/4/192pt_PT
dc.subjectAlgorithmspt_PT
dc.subjectCredit cardpt_PT
dc.subjectDatabasept_PT
dc.subjectFraudpt_PT
dc.subjectFinancial organizationpt_PT
dc.subjectMachine learningpt_PT
dc.subjectDatasetpt_PT
dc.titleSystemic acquired critique of credit card deception exposure through machine learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage20pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title Journal of Open Innovation: Technology, Market, and Complexity,pt_PT
oaire.citation.volume8pt_PT
person.familyNameDantas
person.familyNamefirdaus
person.familyNameJaleel
person.familyNameMata
person.familyNameMata
person.givenNameRui
person.givenNameraheela
person.givenNameFarrokh
person.givenNamePedro
person.givenNameMário Nuno
person.identifier1403614
person.identifier.ciencia-id1413-E166-2CD2
person.identifier.ciencia-idFA13-1761-4192
person.identifier.orcid0000-0001-8566-7303
person.identifier.orcid0000-0001-9827-4568
person.identifier.orcid0000-0002-9788-8725
person.identifier.orcid0000-0001-8465-9539
person.identifier.orcid0000-0003-1765-4273
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationbf484e05-ab4d-4efa-9ccb-ff8a74e0f6a9
relation.isAuthorOfPublication.latestForDiscoverycb2ab661-1901-4eb3-906c-fc00e5a024b0

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