A Review of Deep Learning and Hybrid Techniques for Credit Card Fraud Detection: Trends, Challenges, and Solutions

Fraud detection in online financial transactions has become increasingly important with the rapid growth of digital payment systems and the evolving nature of fraudulent behavior. This literature review examines recent studies on fraud detection, with a specific focus on deep learning and hybrid approaches that combine multiple algorithms to enhance detection accuracy. It examines peer-reviewed works published from 2021 to 2025, emphasizing key trends in the use of deep learning models and their integration with optimization techniques. Frequently used datasets, evaluation metrics, and model architectures are reviewed to provide a comprehensive overview of current practices. In contrast to existing reviews, this study introduces a comparative analysis of several hybrid models, assessing their architectural components, strategies for handling class imbalance, and sequential learning capabilities. Additionally, it offers a critical evaluation of emerging innovations such as attention mechanisms, Generative Adversarial Networks (GANs), and reinforcement learning, highlighting their effectiveness and limitations. The review also identifies core challenges in the field, including the continuous evolution of fraudulent tactics, limitations in available datasets, data imbalance, and the interpretability of models. Finally, the paper discusses potential future directions and emerging solutions that aim to address these gaps. This study seeks to guide researchers and practitioners toward the development of more effective, scalable, and intelligent fraud detection systems by leveraging deep learning and hybrid techniques.