Enterprise Infrastructure, Truist Financial Corporation, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 3248-3259
Received on 18 June 2024; revised on 21 July 2024; accepted on 28 July 2024
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.
Federated Learning; Cross-Border Banking; Predictive Analytics; Privacy-Preserving Machine Learning; Cloud Computing; Financial Risk Assessment; Regulatory Compliance
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Chandrasekhar Anuganti. Deep learning framework for cross-border banking risk assessment: A predictive analytics approach using cloud and AI solutions. World Journal of Advanced Research and Reviews, 2024, 23(1), 3248-3259. Article DOI: https://doi.org/10.30574/wjarr.2024.23.1.2286