Credit card fraud poses significant challenges to financial institutions and consumers worldwide, necessitating robust and effective detection mechanisms. This paper explores the application of machine learning and data science techniques to detect fraudulent activities in credit card transactions. By leveraging advanced algorithms, including supervised and unsupervised learning methods, we aim to identify patterns and anomalies indicative of fraud. We discuss the pre-processing steps, feature engineering, and model selection processes critical to developing an accurate and efficient detection system. Our results demonstrate the potential of machine learning models, such as decision trees, random forests, and neural networks, to enhance fraud detection capabilities, reduce false positives, and protect against financial losses. This research underscores the importance of integrating data science techniques in the fight against credit card fraud, offering insights into future directions and improvements in fraud detection methodologies.

Credit Card Fraud, Machine Learning, Data Science

“Credit Card Fraud Detection Based on Transaction Behaviour –by John Richard D. Kho, Larry A. Vea” published by Proc. of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017

CLIFTON PHUA1, VINCENT LEE1, KATE SMITH1 & ROSS GAYLER2 “ A Comprehensive Survey of Data Mining-based Fraud Detection Research” published by School of Business Systems, Faculty of Information Technology, Monash University, Wellington Road, Clayton, Victoria 3800, Australia

“Survey Paper on Credit Card Fraud Detection by Suman” , Research Scholar, GJUS&T Hisar HCE, Sonepat published by International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3 Issue 3, March 2014

“Research on Credit Card Fraud Detection Model Based on Distance Sum – by Wen-Fang YU and Na Wang” published by 2009 International Joint Conference on Artificial Intelligence

“Credit Card Fraud Detection through Parenclitic Network AnalysisBy Massimiliano Zanin, Miguel Romance, Regino Criado, and SantiagoMoral” published by Hindawi Complexity Volume 2018, Article ID 5764370, 9 pages

“Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy” published by IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 29, NO. 8, AUGUST 2018

“Credit Card Fraud Detection-by Ishu Trivedi, Monika, Mrigya, Mridushi” published by International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 1, January 2016

David J.Wetson,David J.Hand,M Adams,Whitrow and Piotr Jusczak “Plastic Card Fraud Detection using Peer Group Analysis” Springer, Issue 2008.