Credit Card Fraud Detection Using Data Mining

Authors

  • Chithranjaly K S  Department of Computer Application, SNGIST Arts and Science College, North Paravur, Kerala, India
  • Lal Krishna P A  Department of Computer Application, SNGIST Arts and Science College, North Paravur, Kerala, India
  • Radhika B  Department of Computer Application, SNGIST Arts and Science College, North Paravur, Kerala, India

Keywords:

Credit Card Frauds, Data Mining, Genetic Algorithm, Neural Network, KNN algorithm

Abstract

These days, everyone utilizes online administrations for selling or purchasing something. The principle objective of this advancement innovation is to diminish the utilization of actual cash. At this situation, the online deceitful exercises are expanding quickly. Among this, charge card fake exercises arrived at its zenith. To defeat the present situation, distinctive Data Mining strategies can be utilized. These strategies incorporate genetic algorithm, KNN calculation and neural organization. This paper center around the various variants of Master Card frauds and the successful just as effective information mining procedures through Data mining.

References

  1. Arpita Mantri, Chelsi Sen , Dr. Sunil Kumar “An Overview of Credit Card Fraud Detection Using Data Mining Techniques” IJSART - Volume 5 Issue 4 –APRIL 2019, ISSN [ONLINE]: 2395-1052
  2. T.V. Kavipriya , N.Geetha “Study on credit card detection using data mining techniques” ISSN: 2395-5325
  3. Rahul Goyal, Amit Kumar Manjhvar “Review on credit card detection using data mining and machine learning algorithms”
  4. Francisca Nonyelum Ogwueleka“Data mining application in credit card fraud detection system”
  5. T. Kavitha, N.Geetha “An identification and detection of fraudulence in credit ”

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Published

2021-04-20

Issue

Section

Research Articles

How to Cite

[1]
Chithranjaly K S, Lal Krishna P A, Radhika B "Credit Card Fraud Detection Using Data Mining" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 3, pp.24-27, March-April-2021.