AI-Driven Real-Time Fraud Detection in Digital Banking Using Explainable Graph Neural Networks and Behavioral Biometrics

This paper introduces an AI-driven real-time fraud detection system for digital banking that combines explainable graph neural networks with behavioral biometrics to provide accurate, interpretable fraud identification while maintaining customer privacy. The proposed Explainable Banking Fraud Detection System (EBFDS) addresses critical challenges in financial fraud prevention including real-time processing requirements, regulatory compliance, and the need for transparent decision-making in financial services. Our methodology models banking transactions and customer interactions as dynamic graphs where nodes represent accounts, merchants, and devices, while edges capture transaction flows and behavioral patterns. The graph neural network component analyzes complex transaction networks to identify suspicious patterns and money laundering schemes that traditional rule-based systems might miss. We integrate behavioral biometrics including typing patterns, mouse movements, and mobile device interactions to create unique customer profiles that enhance fraud detection accuracy while preserving privacy. The explainable AI component provides clear, auditable explanations for fraud decisions, enabling compliance with financial regulations requiring algorithmic transparency. Our implementation processes over 100,000 transactions per second with sub-100ms decision latency while achieving 96% fraud detection accuracy and reducing false positives by 73%. Experimental validation using anonymized banking datasets from multiple financial institutions demonstrates superior performance in detecting sophisticated fraud schemes including account takeovers, synthetic identity fraud, and coordinated money laundering operations.