Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/5878
Title: Credit Card Fraud Detection
Authors: Soni, Raghav
Nitin [Guided by]
Keywords: E-Commerce
Credit card fraud
Artificial intelligence
Data mining
Fuzzy logic
Machine learning
Credit card
Issue Date: 2015
Publisher: Jaypee University of Information Technology, Solan, H.P.
Abstract: Due 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 criteria
URI: http://ir.juit.ac.in:8080/jspui//xmlui/handle/123456789/5878
Appears in Collections:B.Tech. Project Reports

Files in This Item:
File Description SizeFormat 
Credit Card Fraud Detection.pdf931.49 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.