Hybrid Approach of Credit Card Fraud Detection Based on Outlier Detection
Raman Ghode1, Shiv K. Sahu2, Amit Mishra3

1Raman Ghode, M.Tech Scholar, Technocrats Institute of Technology, Bhopal (M.P.). India.
2Dr. Shiv K. Sahu, Assoc. Professor & Head, Technocrats Institute of Technology, Bhopal (M.P.). India.
3Amit Mishra, M.Tech Coordinator, Technocrats Institute of Technology, Bhopal (M.P.). India.
Manuscript received on April 17, 2016. | Revised Manuscript received on April 24, 2016. | Manuscript published on April 25, 2016. | PP: 34-38 | Volume-4 Issue-4, April 2016. | Retrieval Number: D1096044416
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: financial fraud is constantly rising with the advancement of new technologies and global mediums of the communication, which is resulting in loss of the millions of dollars over the world-wide every year. The organization and the financial firm lose their large volume because of the fraud and the fraudsters that regularly attempting to search for some new rules and strategy to do unauthorized activities. Hence, the fraud detection systems are now became one of the essentials for all the banks that are issuing credit card to decrease their deficit. An efficient use of the techniques of data mining and their algorithms may be applied to find out or to anticipate the fraud by the Knowledge Discovery from the different patterns obtained from collected data-set. In this paper, it is briefly explained regarding the several credit-card deceivers’ strategies and with their methods of detection for the cyber credit-card transactions. The unauthorized transactions get never protected from being got cleared, the organization that should be agreed the economical cost of those types of transaction. This decreases the associated cost with the higher rates of interest and their complaints.
Keywords: Accuracy, Credit Card, Clustering, Classification, Fraud Detection.