Recent Advances in Credit Card Fraud Detection: An Analytical Review of Frameworks, Methodologies, Datasets, and Challenges

Credit card fraud detection (CCFD) remains a critical research domain due to the dynamic, adversarial, and highly imbalanced nature of fraudulent activities in financial systems. This study employs a systematic mapping review guided by the PRISMA 2020 guidelines. It analytically synthesizes 40 peer-reviewed and open-access studies, focusing on methodological trends, machine learning techniques, datasets, optimization strategies, and evaluation metrics. Supervised learning (SL) models, including Random Forest, Decision Trees, Support Vector Machine (SVM), and XGBoost, accounted for nearly half of the reviewed studies and consistently demonstrated strong performance. Deep learning (DL) frameworks, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and their variants, have demonstrated strong capabilities in capturing sequential and high-dimensional patterns of fraud. However, their effectiveness is constrained by class imbalance and dataset bias. Ensemble and hybrid models further enhanced predictive accuracy but introduced higher computational costs and lower interpretability. A key finding is the heavy reliance on the ECCT 2013 dataset (used in more than half of the reviewed studies), which supports reproducibility but limits generalizability to modern fraud contexts. Optimization strategies, such as the Synthetic Minority Oversampling Technique (SMOTE), hyperparameter tuning, and dimensionality reduction, have proven effective in improving recall and reducing false negatives; however, they have been inconsistently applied. Similarly, evaluation metrics were uneven, with accuracy dominating (reported in 75% of studies), while more informative measures such as recall, F1-score, Precision-Recall curves (AUPRC), and Matthews Correlation Coefficient (MCC) received less emphasis despite their relevance to imbalanced data. Overall, while many models achieve high accuracy in controlled environments, their scalability, adaptability, and trustworthiness in real-world deployment remain limited. Future research should prioritize cross-dataset evaluations, standardized metrics, and emerging paradigms such as federated learning, self-supervised approaches, and explainable AI to guide the development of robust and deployable fraud detection systems.