Explainable Update Auditing in Federated Credit Risk Modeling: Bridging Model Transparency and Multi-Party Data Privacy

Abstract

Federated learning enables financial institutions to collaboratively develop credit risk models while maintaining data privacy, yet existing implementations prioritize accuracy and confidentiality over transparency and regulatory compliance requirements. Current federated approaches treat explainability as a secondary concern addressed through separate post-processing workflows, creating significant gaps in auditability and stakeholder trust that limit adoption in regulated environments. This article introduces the Explainable Update Auditing framework, which embeds transparency mechanisms directly into federated training protocols through local explanation bundles and privacy-preserving audit trails. The framework generates standardized, model-agnostic explanations that characterize how institutional updates influence global model behavior without exposing proprietary data or competitive information. Cryptographic attestation mechanisms verify compliance with fairness, stability, and governance constraints throughout training processes using zero-knowledge proof systems that maintain institutional confidentiality while providing mathematical assurance of appropriate collaborative behavior. The dual-layer trust mechanism addresses distinct information needs across multiple stakeholder groups, including participating institutions, regulatory authorities, internal governance bodies, and affected borrowers. Implementation considerations reveal computational overhead challenges, privacy-utility trade-offs, and cryptographic protocol efficiency requirements that must be addressed for practical deployment. The framework transforms federated learning from an opaque collaboration protocol into a transparent, auditable ecosystem that satisfies regulatory requirements while preserving privacy guarantees essential for cross-institutional partnerships in credit risk modeling applications.