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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.