Credit Card Fraud Detection with Machine Learning and Big Data Analytics
Leonidas Theodorakopoulos, Ioanna Kalliampakou, Alexandra Theodoropoulou, Fotini Zakka · 2025
This chapter presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. It evaluates various ML models, including logistic regression, decision trees, random forests, eXtreme gradient boosting and deep convolutional neural networks for their effectiveness in identifying fraudulent activities. The chapter discusses the potential of ensemble methods, graph-powered systems and intelligent sampling in enhancing fraud detection capabilities. It underscores the pivotal role of ML in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions and the broader financial ecosystem. The detection of credit card fraud protects consumers by shielding them from fake transactions, identity theft and financial harm. ML algorithms also support predictive modeling, which can significantly improve an organization's decision-making processes. Decision-making can also be improved by ML algorithms using a tool called the risk scoring system.