As the world is rapidly moving towards digitization and money transactions are becoming cashless, the use of credit cards has rapidly increased. The fraud activities associated with it have also been increasing which leads to a huge loss to the financial institutions. Therefore, we need to analyze and detect the fraudulent transaction from the non-fraudulent ones. In this paper, we present a comprehensive review of various methods used to detect credit card fraud. These methodologies include Hidden Markov Model, Decision Trees, Logistic Regression, Support Vector Machines (SVM), Genetic algorithm, Neural Networks, Random Forests, Bayesian Belief Network. A comprehensive analysis of various techniques is presented. We conclude the paper with the pros and cons of the same as stated in the respective papers.
@article{arxiv.2108.10005,
title = {Credit Card Fraud Detection using Machine Learning: A Study},
author = {Pooja Tiwari and Simran Mehta and Nishtha Sakhuja and Jitendra Kumar and Ashutosh Kumar Singh},
journal= {arXiv preprint arXiv:2108.10005},
year = {2021}
}