Privacy-First Data Platform for Financial Services: Integrating Federated Learning, Differential Privacy, and Immutable Audit Trails

We present a privacy-preserving data platform designed for financial institutions that integrates three complementary privacy-enhancing technologies: Federated Learning, Differential Privacy, and blockchain-anchored immutable audit trails. Our system enables multiple financial institutions to collaboratively improve AI model accuracy while maintaining strict data isolation and regulatory compliance. We demonstrate that our approach achieves 94.2% of centralized training accuracy while providing formal privacy guarantees (ε=0.1 differential privacy) and complete auditability for regulatory requirements. The platform has been implemented and deployed, comprising over 220,000 lines of production code across 15 modules.