A Review on Credit Card Fraud Detection Techniques

Authors

  • Chandan Kumar  BE Students, Department of Information Technology/Computer Science & Engineering, J. D College of Engineering and Management, Nagpur, Maharashtra, India
  • Kamlesh Parate  Department of Information Technology/Computer Science & Engineering, J. D College Of Engineering and Management, Nagpur, Maharashtra, India
  • Shreyash Sahare  
  • Prajakta Lokhande  
  • Moh. Akram Beg  
  • Prof. Rohan Kokate  

Keywords:

Credit Card Fraud, Hidden Markov Model (HMM), Fraud Detection, Password, Security Question

Abstract

The credit card has turned into the most prevalent method of instalment for both online and additionally normal buy, in instances of fraud related with it are likewise rising. Credit card frauds are expanding step by step paying little respect to different methods created for its detection. Fraudsters are experts to the point that they create better approaches for conferring fraudulent exchanges every day which requests consistent advancement for its detection methods. A large portion of the systems in light of Artificial Intelligence, Fuzzy Logic, Neural Network, Logistic Regression, Naive Bayesian, Machine Learning, Sequence Alignment, Decision tree, Bayesian system, meta learning, Genetic programming and so on., these are developed in recognizing different credit card fraudulent exchanges. This paper displays a review of different methods utilized as a part of different credit card fraud detection systems.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-01-30

Issue

Volume 3, Issue 2, January-February-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Chandan Kumar, Kamlesh Parate, Shreyash Sahare, Prajakta Lokhande, Moh. Akram Beg, Prof. Rohan Kokate, " A Review on Credit Card Fraud Detection Techniques, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 2, pp.253-257, January-February-2018.