Zero-Trust Data Warehousing for Cross-Bank AI Collaboration: A Technical Review

Abstract

The financial services industry faces unprecedented challenges in combating sophisticated financial crimes while maintaining competitive advantages and regulatory compliance. Zero-trust data warehousing architectures combined with advanced privacy-preserving technologies present transformative opportunities for cross-bank AI collaboration. The convergence of confidential computing, homomorphic encryption, and federated learning within governed warehouse fabrics represents a paradigm shift in how financial institutions collaborate on AI initiatives. This technical review examines the implementation of zero-trust security models within federated data warehousing environments to enable secure, governed multi-party AI training, focusing particularly on fraud detection and Anti-Money Laundering operations where collective intelligence significantly enhances detection capabilities while maintaining strict data privacy and regulatory compliance. The zero-trust architecture operates on fundamental principles of continuous verification across all network transactions, demonstrating substantial reductions in successful breach attempts and significant improvements in threat detection response times. Federated learning enables model training across distributed datasets without centralizing data, allowing institutions to train sophisticated models using collective insights while preserving privacy. Advanced homomorphic encryption schemes provide mathematical foundations for performing computations on encrypted data without decryption, enabling institutions to share encrypted insights while maintaining complete privacy. The integration creates comprehensive privacy-preserving frameworks that address various attack vectors and provide defense-in-depth security for collaborative initiatives.