Secure AI Model Sharing in Financial Institutions via Blockchain Federated Networks

Abstract

In the modern financial ecosystem, AI becomes a third-degree spirit for applications like fraud detection, credit scoring, and risk modeling. Due to the strict nature of privacy regulations, concerns regarding data sovereignty, and an existing competitive wall, development of collaborative AI within financial institutions is largely hindered. FL in literature has emerged as a potential mechanism for decentralized model training without the exchange of raw data. FL alone does not guarantee trust, accountability, and verifiability in applications involving multiple stakeholders.

This vision paper develops a further methodology by proposing a blockchain-empowered federated learning architecture for secure AI model sharing within financial institutions. The proposed architecture, combining an immutable blockchain ledger and smart contracts with FL workflows, addresses key challenges such as tampering with a model, attribution of ownership to the model, and auditing for compliance. Smart contracts enforce access control policies and validate model contributions, and in turn, the blockchain ledger logs model updates to allow transparent tracing. We emulate the federated learning scenario using TensorFlow Federated on synthetic financial datasets coupled with Hyperledger Fabric for blockchain operations.

The experiments reveal that the system works towards optimizing the application of the model while at the same time optimizing its security, accountability, and interoperability. Furthermore, the integration of a blockchain introduces a negligible latency overhead and scales well as more institutional nodes join. This architecture therefore aligns well with emerging privacy regulations and offers a promising pathway for secure, instant, and auditable collaboration on AI models across financial networks.

This study contributes an AI sharing mechanism that is secure by design to balance data privacy, institutional trust, and auditability, laying the foundation for resilient and collaborative financial AI ecosystems.