Artykuł  

Detection of credit card fraud with optimized deep neural network in balanced data condition

Krótki opis
creativeworkseries.issn1508-2806
dc.contributor.authorShome, Nirupam
dc.contributor.authorSarkar, Devran Dey
dc.contributor.authorKashyap, Richik
dc.contributor.authorLaskar, Rabul Hussain
dc.date.issued2024
dc.description.abstractDue to the huge number of financial transactions, it is almost impossible for humans to manually detect fraudulent transactions. In previous work, the datasets are not balanced and the models suffer from overfitting problems. In this paper, we tried to overcome the problems by tuning hyperparameters and balancing the dataset with a hybrid approach using under-sampling and oversampling techniques. In this study, we have observed that these modifications are effective in getting better performance in comparison to the existing models. The MCC score is considered an important parameter in binary classification since it ensures the correct prediction of the majority of positive data instances and negative data instances. So, we emphasize on MCC score and our method achieved an MCC score of 97.09%, which is far more (16 % approx.) than other state-of-the-art methods. In terms of other performance metrics, the result of our proposed model has also improved significantly.en
dc.description.placeOfPublicationKraków
dc.description.versionwersja wydawnicza
dc.identifier.doihttps://doi.org/10.7494/csci.2024.25.2.5967
dc.identifier.eissn2300-7036
dc.identifier.issn1508-2806
dc.identifier.urihttps://repo.agh.edu.pl/handle/AGH/110646
dc.language.isoeng
dc.publisherWydawnictwa AGH
dc.rightsAttribution 4.0 International
dc.rights.accessotwarty dostęp
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcode
dc.subjectcredit carden
dc.subjectfraud detectionen
dc.subjectdeep learningen
dc.subjectfraud transactionsen
dc.titleDetection of credit card fraud with optimized deep neural network in balanced data conditionen
dc.title.relatedComputer Scienceen
dc.typeartykuł
dspace.entity.typePublication
publicationissue.issueNumberNo. 2
publicationissue.paginationpp. 253-276
publicationvolume.volumeNumberVol. 25
relation.isJournalIssueOfPublication13159f87-dd51-47a1-97e0-56e2d9693c18
relation.isJournalIssueOfPublication.latestForDiscovery13159f87-dd51-47a1-97e0-56e2d9693c18
relation.isJournalOfPublication020291ee-249b-4dcf-98a3-276a2f7981aa
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