vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff0bc80b000000d48d010001000500 Along with the great increase of internet and e-commerce, the use of credit card is an unavoidable one. Due to the increase of credit card usage, the frauds associated with this have also increased. There are a lot of approaches used to detect the frauds. In this paper, behavior based classification approach using Support Vector Machines are employed and efficient feature extraction method also adopted. If any discrepancies occur in the behaviors transaction pattern then it is predicted as suspicious and taken for further consideration to find the frauds. Generally credit card fraud detection problem suffers from a large amount of data, which is rectified by the proposed method. Achieving finest accuracy, high fraud catching rate and low false alarms are the main tasks of this approach.
V. Dheepa1 , R. Dhanapal2
Bharathiar University, India1, Easwari Engineering College, India2
Data Mining, Classification, Fraud Detection, Support Vector Machine, E-commerce
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 2 , Issue: 4 , Pages: 391-397 )
Date of Publication :
July 2012
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