Rodyti trumpą aprašą

dc.rights.licenseKūrybinių bendrijų licencija / Creative Commons licenceen_US
dc.contributor.authorPratuzaitė, Greta
dc.contributor.authorMaknickienė, Nijolė
dc.date.accessioned2024-05-22T08:07:16Z
dc.date.available2024-05-22T08:07:16Z
dc.date.issued2020
dc.date.submitted2020-04-16
dc.identifier.isbn9786094762314en_US
dc.identifier.issn2029-4441en_US
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/154256
dc.description.abstractCriminal financial behaviour is a problem for both banks and newly created fintech companies. Credit card fraud detection becomes a challenge for any such company. The aim of this paper is to com-pare ability to detect credit card fraud by four algorithmic methods: Generalized method of moments, K-nearest neighbour, Naive Bayes classification and Deep learning. The deep learning algorithm has been tuned to select key parameters so that fraud detection accuracy is the best. Five recognition accuracy pa-rameters and a cost calcualtions showed that the deep learning algorithm is the best fraud detection meth-od compared to other classification algorithms. A financial company reduces losses and increases custom-er confidence by using fraud prevention technologies.en_US
dc.format.extent8 p.en_US
dc.format.mediumTekstas / Texten_US
dc.language.isoenen_US
dc.relation.urihttps://etalpykla.vilniustech.lt/handle/123456789/154212en_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.source.urihttps://bm.vgtu.lt/index.php/verslas/2020/paper/view/558en_US
dc.subjectfraud detectionen_US
dc.subjectclassificationen_US
dc.subjectcredit cardsen_US
dc.subjectFinTechen_US
dc.subjectconfusion matrixen_US
dc.subjectlosesen_US
dc.subjectdeep learningen_US
dc.titleInvestigation of credit cards fraud detection by using deep learning and classification algorithmsen_US
dc.typeKonferencijos publikacija / Conference paperen_US
dcterms.accessRightsLaisvai prieinamas / Openly availableen_US
dcterms.accrualMethodRankinis pateikimas / Manual submissionen_US
dcterms.alternativeFinance: new challenges, new opportunitiesen_US
dcterms.dateAccepted2020-05-06
dcterms.issued2020-05-08
dcterms.licenseCC BYen_US
dcterms.references30en_US
dc.description.versionTaip / Yesen_US
dc.contributor.institutionVilniaus Gedimino technikos universitetasen_US
dc.contributor.institutionVilnius Gediminas Technical Universityen_US
dc.contributor.facultyVerslo vadybos fakultetas / Faculty of Business Managementen_US
dc.contributor.departmentFinansų inžinerijos katedra / Department of Financial Engineeringen_US
dcterms.sourcetitle11th International Scientific Conference “Business and Management 2020”en_US
dc.identifier.eisbn9786094762307en_US
dc.identifier.eissn2029-929Xen_US
dc.publisher.nameVilnius Gediminas Technical Universityen_US
dc.publisher.nameVilniaus Gedimino technikos universitetasen_US
dc.publisher.countryLithuaniaen_US
dc.publisher.countryLietuvaen_US
dc.publisher.cityVilniusen_US
dc.identifier.doihttps://doi.org/10.3846/bm.2020.558en_US


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