Banking Fraud Detection in Financial Transactions: A Systematic Review of Representative Methods

Financial fraud poses an evolving threat to modern economies, requiring advanced detection solutions beyond traditional rule-based systems. This systematic literature review follows established guidelines, analyzing 103 selected studies published between 2021 and 2025 to capture recent advancements in artificial intelligence for banking transaction fraud detection. The review synthesizes 20 representative methods across four primary computational paradigms: enhanced machine learning, deep learning, ensemble techniques, and hybrid frameworks. While AI models report standard accuracy rates of 95–99%, such metrics can be misleading due to severe class imbalance; evaluated models demonstrate more meaningful performance with F1-scores and AUC-ROC values ranging between 85% and 99% depending on experimental setups. Financial institutions should transition from traditional systems to AI-powered solutions, address class imbalance with specialized resampling or generative strategies, prioritize real-time detection infrastructure, and adopt hybrid or ensemble architectures to optimize performance and lower misclassification costs. This technological evolution marks a fundamental shift in modern financial security and fraud‑prevention efficacy.