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-enhancingtechnologies: Federated Learning, Differential Privacy, and blockchain-anchored immutable audit trails. Our system enables multiplefinancial institutions to collaboratively improve AI model accuracy while maintaining strict data isolation and regulatory compliance. Wedemonstrate that our approach achieves 94.2% of centralized training accuracy while providing formal privacy guarantees (ε=0.1 differentialprivacy) and complete auditability for regulatory requirements. The platform has been implemented and deployed, comprising over 220,000lines of production code across 15 modules.