A Comparative Analysis of Various Credit Card Fraud Detection Techniques
Yashvi Jain1, NamrataTiwari2, ShripriyaDubey3, Sarika Jain4
1Yashvi Jain, Department of Computer Applications, National Institute of Technology, (Haryana), India.
2NamrataTiwari, Department of Computer Applications, National Institute of Technology, (Haryana), India.
3ShripriyaDubey, Department of Computer Applications, National Institute of Technology, (Haryana), India.
4Sarika Jain, Department of Computer Applications, National Institute of Technology, (Haryana), India.
Manuscript received on 08 February 2019 | Revised Manuscript received on 21 February 2019 | Manuscript Published on 04 March 2019 | PP: 402-407 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES2073017519/19©BEIESP
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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: Fraud is any malicious activity that aims to cause financial loss to the other party. As the use of digital money or plastic money even in developing countries is on the rise so is the fraud associated with them. Frauds caused by Credit Cards have costs consumers and banks billions of dollars globally. Even after numerous mechanisms to stop fraud, fraudsters are continuously trying to find new ways and tricks to commit fraud. Thus, in order to stop these frauds we need a powerful fraud detection system which not only detects the fraud but also detects it before it takes place and in an accurate manner. We need to also make our systems learn from the past committed frauds and make them capable of adapting to future new methods of frauds. In this paper we have introduced the concept of frauds related to credit cards and their various types. We have explained various techniques available for a fraud detection system such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Network, K- Nearest Neighbour (KNN), Hidden Markov Model, Fuzzy Logic Based System and Decision Trees. An extensive review is done on the existing and proposed models for credit card fraud detection and has done a comparative study on these techniques on the basis of quantitative measurements such as accuracy, detection rate and false alarm rate. The conclusion of our study explains the drawbacks of existing models and provides a better solution in order to overcome them.
Keywords: Neural Network, Genetic Algorithm, Support Vector Machine, Bayesian Network, K- Nearest Neighbour, Hidden Markov Model, Fuzzy Logic Based System, Decision Trees.
Scope of the Article: Predictive Analysis