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dc.contributor.authorMabani, Courage
dc.contributor.authorChristou, Nikolaos
dc.contributor.authorKatkov, Sergey
dc.date.accessioned2022-12-12T12:07:18Z
dc.date.available2022-12-12T12:07:18Z
dc.date.issued2022-12-31
dc.identifier.citationMabani C, Christou N, Katkov S (2022) 'Detection of credit card frauds with machine learning solutions: an experimental approach', Computing Conference on Intelligent Computing - Online, Springer.en_US
dc.identifier.isbn9783031104619
dc.identifier.issn2367-3370
dc.identifier.doi10.1007/978-3-031-10461-9_49
dc.identifier.urihttp://hdl.handle.net/10547/625605
dc.description.abstractIn many cases frauds in payment transactions could be detected by analysing the customer’s behaviour. Only in the United States fraudulent transactions led to financial losses of 300 billion a year. Machine learning (ML) and Data Mining techniques were shown to be efficient for detection of fraudulent transactions. This paper proposes an experimental way for designing a ML solution to the problem, which allows practitioners to minimise financial losses by analysing the customer’s behaviour and common patterns of using credit cards. The solution designed within a Random Forest (RF) strategy is examined on a public data set available for the research community. The results obtained on the benchmark data show that the proposed approach provides a high accuracy of detecting fraudulent transaction based on the customer’s behaviour patterns that were learnt from data. This allow us to conclude that the use of the RF models for detecting credit card fraud transactions allows practitioners to design an efficient solution in terms of sensitivity and specificity. Our experimental results show that practitioners using the RF models can find new insights into the problem and minimise the losses.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.urlhttps://link.springer.com/chapter/10.1007/978-3-031-10461-9_49en_US
dc.subjectcredit card frauden_US
dc.subjectmachine learningen_US
dc.titleDetection of credit card frauds with machine learning solutions: an experimental approachen_US
dc.title.alternativeLecture Notes in Networks and Systemsen_US
dc.typeConference papers, meetings and proceedingsen_US
dc.identifier.eissn2367-3389
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.identifier.journalINTELLIGENT COMPUTING, VOL 1en_US
dc.date.updated2022-12-12T12:05:14Z
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