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This paper explores the role of AI in enhancing financial risk prediction and fraud detection in banking systems.

May 4, 2026Open Access

Artificial Intelligence Driven Financial Risk Prediction and Fraud Detection in Modern Banking Systems

Key Points

This paper explores the role of AI in enhancing financial risk prediction and fraud detection in banking systems.
Review of applications of artificial intelligence techniques such as machine learning, deep learning, and ensemble methods.
Analysis of literature regarding the effectiveness and challenges of AI in banking.
Evaluation of improvements in detection accuracy and reduction in false positives.
AI systems significantly enhance detection accuracy and outperform traditional methods.
Improvements in real-time identification of risks and fraudulent activities were noted.
Challenges including model explainability, data privacy, and regulatory compliance were identified.

Abstract

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.

Artificial Intelligence Driven Financial Risk Prediction and Fraud Detection in Modern Banking Systems

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