A Survey on Different Data Mining & Machine Learning Methods for Credit Card Fraud Detection

Authors(2) :-Vipul Patil, Dr. Umesh Kumar Lilhore

Due to rapid growth in field of cashless or digital transactions, credit cards are widely used in all around the world. Credit cards providers are issuing thousands of cards to their customers. Providers have to ensure all the credit card users should be genuine and real. Any mistake in issuing a card can be reason of financial crises. Due to rapid growth in cashless transaction, the chances of number of fraudulent transactions can also increasing. A Fraud transaction can be identified by analyzing various behaviors of credit card customers from previous transaction history datasets. If any deviation is noticed in spending behavior from available patterns, it is possibly of fraudulent transaction. Data mining and machine learning techniques are widely used in credit card fraud detection. In this survey paper we are presenting review of various data mining and machine learning methods which are widely used for credit card fraud detections.

Authors and Affiliations

Vipul Patil
M. Tech Scholar Department of CSE NIIST Bhopal, Madhya Pradesh, India
Dr. Umesh Kumar Lilhore
Head Department of CSE NIIST Bhopal, Madhya Pradesh, India

Data Mining, Machine Learning, Credit Card Fraud, Cashless Transactions.

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 320-325
Manuscript Number : CSEIT183565
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Vipul Patil, Dr. Umesh Kumar Lilhore , "A Survey on Different Data Mining & Machine Learning Methods for Credit Card Fraud Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.320-325, May-June-2018.
Journal URL : http://ijsrcseit.com/CSEIT183565

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