Credit Card Fraud Detection Using Machine Learning

Abstract

Credit card fraud detection is a critical area of concern for financial institutions, as it aims to identify and prevent unauthorized transactions to safeguard consumers' financial assets. With the rapid growth of e-commerce and digital payments, fraudsters have increasingly employed sophisticated methods to exploit vulnerabilities in payment systems. This paper explores the various techniques used for credit card fraud detection, focusing on machine learning algorithms, statistical models, and hybrid approaches. We discuss the challenges in detecting fraud, such as the imbalance between legitimate and fraudulent transactions, the dynamic nature of fraud tactics, and the need for real-time detection. Additionally, we analyze the role of data preprocessing, feature engineering, and the use of advanced methods such as deep learning, ensemble methods, and anomaly detection to improve detection accuracy and reduce false positives. Finally, the paper reviews the impact of emerging technologies such as blockchain and AI on the future of fraud detection, providing a comprehensive overview of the current state and future directions in this field.

Keywords: Credit card fraud detection, Flask Web application, Anomaly detection, Ensemble methods