Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2096
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dc.contributor.authorThennakoon, A-
dc.contributor.authorBhagyani, C-
dc.contributor.authorPremadasa, S-
dc.contributor.authorMihiranga, S-
dc.contributor.authorKuruwitaarachchi, N-
dc.date.accessioned2022-04-29T05:25:15Z-
dc.date.available2022-04-29T05:25:15Z-
dc.date.issued2019-01-10-
dc.identifier.citationA. Thennakoon, C. Bhagyani, S. Premadasa, S. Mihiranga and N. Kuruwitaarachchi, "Real-time Credit Card Fraud Detection Using Machine Learning," 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2019, pp. 488-493, doi: 10.1109/CONFLUENCE.2019.8776942.en_US
dc.identifier.isbn978-1-5386-5933-5-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2096-
dc.description.abstractCredit card fraud events take place frequently and then result in huge financial losses [1]. The number of online transactions has grown in large quantities and online credit card transactions holds a huge share of these transactions. Therefore, banks and financial institutions offer credit card fraud detection applications much value and demand. Fraudulent transactions can occur in various ways and can be put into different categories. This paper focuses on four main fraud occasions in real-world transactions. Each fraud is addressed using a series of machine learning models and the best method is selected via an evaluation. This evaluation provides a comprehensive guide to selecting an optimal algorithm with respect to the type of the frauds and we illustrate the evaluation with an appropriate performance measure. Another major key area that we address in our project is real-time credit card fraud detection. For this, we take the use of predictive analytics done by the implemented machine learning models and an API module to decide if a particular transaction is genuine or fraudulent. We also assess a novel strategy that effectively addresses the skewed distribution of data. The data used in our experiments come from a financial institution according to a confidential disclosure agreement.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence);Pages 488-493-
dc.subjectReal-timeen_US
dc.subjectCredit Carden_US
dc.subjectFraud Detectionen_US
dc.subjectMachine Learningen_US
dc.titleReal-time credit card fraud detection using machine learningen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/CONFLUENCE.2019.8776942en_US
Appears in Collections:Department of Computer Systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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