Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/5878
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dc.contributor.authorSoni, Raghav-
dc.contributor.authorNitin [Guided by]-
dc.date.accessioned2022-08-18T10:48:15Z-
dc.date.available2022-08-18T10:48:15Z-
dc.date.issued2015-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui//xmlui/handle/123456789/5878-
dc.description.abstractDue to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteriaen_US
dc.language.isoenen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectE-Commerceen_US
dc.subjectCredit card frauden_US
dc.subjectArtificial intelligenceen_US
dc.subjectData miningen_US
dc.subjectFuzzy logicen_US
dc.subjectMachine learningen_US
dc.subjectCredit carden_US
dc.titleCredit Card Fraud Detectionen_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

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