Cross-border banking systems face unprecedented challenges in implementing predictive analytics while maintaining regulatory compliance and data sovereignty requirements. Traditional centralized approaches to risk assessment and fraud detection are inadequate due to regulatory constraints that prohibit cross-border data sharing and the increasing sophistication of financial crimes. This research addresses the critical gap in privacy-preserving predictive analytics for international banking networks by proposing a federated deep learning framework that enables collaborative model training without compromising data locality requirements. The proposed methodology integrates cloud-based federated learning with secure multiparty computation protocols, allowing financial institutions to benefit from collective intelligence while maintaining strict data governance. Our experimental validation across a simulated network of 12 international banks demonstrates superior predictive performance with 94.7% accuracy in fraud detection, 89.3% precision in credit risk assessment, and 91.8% recall in anti-money laundering detection, representing improvements of 12.4%, 8.7%, and 15.2% respectively over traditional isolated models. The framework successfully maintains data privacy through differential privacy mechanisms while achieving convergence within 150 federated rounds. These findings establish a new paradigm for international financial collaboration, enabling enhanced risk management capabilities without violating data sovereignty regulations.
Chandrasekhar Anuganti (Tue,) studied this question.