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A Hybrid Approach for Credit Card Fraud Detection using Rough Set and Decision Tree Technique
Author(s) -
Rajni Jain,
Bhupesh Gour,
Surendra Dubey
Publication year - 2016
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/ijca2016909325
Subject(s) - computer science , decision tree , credit card fraud , credit card , rough set , set (abstract data type) , tree (set theory) , data mining , decision tree learning , machine learning , artificial intelligence , world wide web , mathematics , payment , programming language , mathematical analysis
To make the business accessible to a large number of customers worldwide, many companies small and big have put up their presence on the internet. Online businesses gave birth to e-commerce platforms which in turn use digital modes of transaction such as credit-card, debit card etc. This kind of digital transaction attracted millions of users to transact on the internet. Along came the risk of online credit card frauds. Hence the need to have secure payment transactions arose and many techniques based on Neural Network, Decision Tree, Artificial Intelligence, Artificial Immune System, Fuzzy based systems, Nearest neighbor algorithm, Support Vector Machines, Genetic Algorithm were developed to detect the fraudulent online credit card transactions. This paper presents hybrid Approach for Credit Card Fraud detection using Rough Set and Decision Tree Technique which can be used in credit card fraud detection mechanisms. General Terms Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.

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