Credit Card Fraud Detection using Machine Learning: A Study

Pooja Tiwari; Simran Mehta; Nishtha Sakhuja; Jitendra Kumar; Ashutosh Kumar Singh

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arXiv · 2108.10005

Credit Card Fraud Detection using Machine Learning: A Study

Pooja Tiwari·Simran Mehta·Nishtha Sakhuja·Jitendra Kumar·Ashutosh Kumar Singh

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Abstract

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.

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Pooja Tiwari, Simran Mehta, Nishtha Sakhuja, Jitendra Kumar, Ashutosh Kumar Singh. 2021-08-23. Credit Card Fraud Detection using Machine Learning: A Study. https://arxiv.org/abs/2108.10005

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