Deep Learning Architecture for Banking Risk Management: Cloud and AI-Driven Predictive Analytics Solution

The increasing globalization of financial services has intensified the complexity of cross-border banking risk management, particularly under stringent regulatory and data sovereignty constraints. Conventional predictive analytics frameworks rely on centralized data aggregation, rendering them incompatible with modern privacy regulations such as GDPR, CCPA, and jurisdiction-specific banking secrecy laws. This paper presents a privacy-preserving federated deep learning architecture designed to enable collaborative risk assessment across international banking institutions without violating data locality requirements. The proposed framework integrates cloud-native federated learning, secure aggregation, differential privacy, and compliance-aware orchestration to support fraud detection, credit risk assessment, and anti-money laundering analytics. Experimental validation conducted across a simulated multinational banking network demonstrates statistically significant improvements in predictive accuracy, recall, and robustness over isolated and centralized baselines, while maintaining formal privacy guarantees and regulatory auditability. The findings establish a scalable and regulation-aligned paradigm for collaborative financial intelligence, enabling enhanced cross-border risk visibility without compromising confidentiality or governance.