Fraud Detection In Banking Leveraging Ai To Identify And Prevent Fraudulent Activities In Real-Time

Abstract

Fraud detection in banking has advanced significantly with the integration of Artificial Intelligence (AI), enabling real-time identification and prevention of fraudulent activities. This systematic review, based on 112 peer-reviewed articles, follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to explore state-of-the-art AI techniques employed in banking fraud detection. A structured search and analysis of scholarly databases identified key approaches categorized into supervised, unsupervised, and hybrid learning models. These models were evaluated for their effectiveness in detecting transaction anomalies, account takeovers, and identity theft. Emphasis is placed on real-time capabilities, leveraging machine learning algorithms such as neural networks, decision trees, and ensemble models, alongside advanced methods like deep learning and reinforcement learning. Key challenges identified include data imbalance, evolving fraud patterns, and privacy concerns. Mitigation strategies, such as feature engineering, anomaly detection frameworks, and privacy-preserving techniques, were reviewed for their ability to address these issues. The findings highlight the transformative role of AI in improving detection accuracy, minimizing false positives, and enhancing operational efficiency. This review also identifies critical research gaps, such as the absence of standardized benchmarks and limited scalability of current AI systems, and explores future directions, including the integration of AI with blockchain and federated learning to enhance security and transparency. By synthesizing insights from the analyzed articles, this study provides actionable recommendations for researchers and practitioners to advance AI-driven fraud prevention in the banking sector.