Analysis of Credit Card Fraud Detection Techniques

Sunil Bhatia, Rashmi Bajaj, Santosh Hazari

Abstract: Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. In an era of digitalization, credit card fraud detection is of great importance to financial institutions. In this paper, we analyze credit card fraud detection using different techniques Bayesian Learning, BLAST-SSAHA Hybridization, Hidden Markov Model, Fuzzy Darwinian detection, Neural Networks, SVM, K-Nearest Neighbour and Nave Bayes. After analyzing through each technique, our aim is to compare all the techniques based on some parameters. The obtained results from databases of credit card transactions show the power of these techniques in the fight against banking fraud comparing them to others in the same field.

Keywords: Machine Learning, Neural Networks, Blast SSAHA Hybridization, Fuzzy Darwinian Detection

How to Cite?: Sunil Bhatia, Rashmi Bajaj, Santosh Hazari, "Analysis of Credit Card Fraud Detection Techniques", Volume 5 Issue 3, March 2016, International Journal of Science and Research (IJSR), Pages: 1302-1307, https://www.ijsr.net/getabstract.php?paperid=NOV162099, DOI: https://dx.doi.org/10.21275/NOV162099