Explainable Federated Learning Model for Cross-Institutional Digital Financial Data Collaboration and Risk Control.

Abstract

The fragmentation of financial data across institutions and stringent privacy regulations severely hinder collaborative risk management, leading to suboptimal fraud detection with accuracy below 60% and high SME loan default errors exceeding 25%. Existing federated learning solutions face challenges with Non-IID data, model interpretability, and compliance, resulting in real-world adoption rates below 20%. To address these gaps, we propose FedRisk, an explainable federated learning framework integrating four key innovations. First, a cross-institutional data mesh with ontological semantic harmonization achieves feature utilization above 85%. Second, a hybrid AI model combines locally interpretable GBDT with monotonic constraints and a global attention-based Transformer, reducing performance loss in Non-IID scenarios by 7.6% compared to FedAvg. Third, a tripartite privacy mechanism employs adaptive differential privacy with a total budget of ε=5, additive secret sharing, and fairness-aware regularization, limiting group prediction bias below 0.05 and RAROC loss under 2.1%. Fourth, communication-efficient optimizations reduce per-round bandwidth to 8.5 MB and training time to 85 min. Evaluated on over 5 million multi-institutional records, FedRisk achieves an AUC-ROC of 0.912, only 1.8% lower than centralized GBDT, while resisting membership inference attacks with an AUC of 0.58. It reduces cross-institutional data transmission by 99% and outperforms classic FL baselines by 7.3% in AUC-ROC. By enabling secure, transparent, and regulation-compliant risk prediction without raw data sharing, FedRisk provides a scalable solution for cross-institutional financial collaboration.