Fraud Detection in Financial Transactions: A Survey of Machine Learning Models and their Applications in Credit Card Security

The rapid explosion in e-transactions has also been accompanied by an equal explosion in credit card fraud (CCF), and this is seriously raising money and security issues with individuals as well as banks. Rule-based anti-fraud solutions are susceptible to failing to recognize advanced fraudulent patterns and thus the requirement to incorporate advanced machine learning (ML) algorithms. This review covers a broad spectrum of supervised and unsupervised learning methods, i.e., AdaBoost, and ensemble methods, in light of their capacity to identify fraudulent transactions. An elaborate discussion of most important F1-score, and AUC is done by utilizing benchmark datasets such as the European Credit Card Dataset. The paper is concentrating on the difficulties in fraud detection within real-world use cases, such as multi-modal class distribution overlap, extremely imbalanced class data, model interpretability, and real-time processing requirements. Ensemble and stacking models were found to perform outstanding accuracy and credibility, while real-time distributed processing design and explainable AI are still central to wider adoption. Deep learning, federated learning, and hybrid blockchain technologies are to increase security, transparency, and scalability. In conclusion, this review discusses the prospects of ML in creating stable, smart, and dynamic CCF detection systems.