Abstract

The increasing digitization of financial systems has amplified exposure to sophisticated cyber-fraud, including identity theft, social-engineering fraud, account takeover, and transactional anomalies. Traditional rule-based fraud detection methods struggle to cope with the scale and complexity of evolving financial threats. This paper presents AI-Driven Transaction Shield (AITS), an adaptive security framework that deploys multi-layered defense across device, network, and transaction intelligence. The proposed approach integrates real-time anomaly detection, graph-based fraud pattern correlation, and user-behavior risk scoring to predict and mitigate high-risk transactions before execution. AITS employs federated learning to preserve data privacy while improving fraud-intelligence models across institutions. Experiments conducted on anonymized transaction datasets demonstrate enhanced fraud-prediction accuracy and reduced false positives compared with traditional detection systems. Results show significant improvements in detection precision and response automation, enabling secure and seamless financial operations. The findings highlight the viability of AI-integrated financial security architectures in safeguarding digital economies and building user trust. AITS contributes a scalable, proactive strategy against emerging cyber-financial threats while aligning with regulatory mandates on secure transaction processing.