Electronic Credit Card Fraud Detection System by Collaboration of Machine Learning Models
Shiv Shankar Singh

Shiv Shankar Singh, Department of Computer Science and Engineering, Sanskriti University, (Uttar Pradesh), India. 

Manuscript received on 04 October 2019 | Revised Manuscript received on 18 October 2019 | Manuscript Published on 26 December 2019 | PP: 92-94 | Volume-8 Issue-12S October 2019 | Retrieval Number: L102810812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1028.10812S19

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© The Authors. 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: In the financial industrial sector the lightning growth and participation of internet-based transactional events give rise to malicious activities like a fraud that result in financial loss. The malicious activities have no continuous pattern their pattern, behavior, working always keep on changing with the increasing growth in technology. Every time a new technology comes in the market the hoaxer study about that technology and implement malicious activity through the learned technology and internet-based activities. The hoaxer analyzes the behavior patterns of consumers to execute the plan of fraud to cause loss to the consumer. So to overcome this problem of fraud, hoax, cheat in the financial sector a fraud identification system is needed to identify the cheating, fraud and alike activities in internet-based money transactions by employing machine learning techniques. This presented paper focuses on fraud activities that cannot be detected manually by carrying out research and examine the results of logistic regression, decision tree and support vector machine. A dataset of electronic payment card is taken from European electronic cardholders, the machine learning techniques are applied on the unstructured and process-free data.

Keywords: Fraud in Credit Card, Data Mining, Logistic Regression, Decision Tree, SVM.
Scope of the Article: Machine Learning