Artificial Intelligence (AI) has become a transformative force in modern banking, particularly in financial risk prediction and fraud detection. This paper reviews the applications of machine learning (ML), deep learning (DL), and advanced techniques such as XGBoost, neural networks, and ensemble models in identifying risks and fraudulent activities in real time. AI systems significantly improve detection accuracy, reduce false positives, and enable predictive risk management, often outperforming traditional rule-based approaches. However, challenges related to model explainability, data privacy, bias, and regulatory compliance persist. The review synthesizes recent literature and highlights both achievements and future directions.
Gulsanam Kimsanova (2026) studied this question.