Advanced Machine Learning and Deep Learning Approaches for Credit Card Fraud Detection: A Comprehensive Review and Performance

Abstract

The rapid growth of online transactions and digital payment systems has significantly increased the risk of credit card fraud, posing serious financial and security challenges for banking institutions and customers. Traditional machine learning techniques often struggle to detect complex, evolving, and highly imbalanced fraud patterns, resulting in reduced detection accuracy and delayed responses. Recent advancements in deep learning, federated learning, attention mechanisms, and graph-based models have shown promising improvements in fraud detection performance while addressing privacy, scalability, and adaptability issues. This study presents a comprehensive review of state-of-the-art credit card fraud detection techniques, including federated deep learning models, attention-enhanced neural networks, prototype-based learning, ensemble methods, and feature selection strategies. The analysis highlights key contributions, datasets, and performance outcomes of existing approaches, emphasizing their strengths and limitations. Furthermore, the review identifies persistent challenges such as computational complexity, concept drift, class imbalance, and feature redundancy. The findings suggest that hybrid and privacy-preserving deep learning models offer superior accuracy, robustness, and practical applicability for real-world fraud detection systems, paving the way for future research toward efficient and adaptive fraud prevention solutions.