Privacy-Preserving Federated Learning for Collaborative Risk Monitoring Across Financial Institutions: Balancing Regulatory Compliance and Intelligence Sharing

Financial institutions today face growing pressure to balance data privacy protection with the sharing of risk intelligence across organizations. This paper offers an in-depth analysis of how privacy-preserving federated learning techniques can be applied to cross-institutional financial risk monitoring. At the core of the proposed framework is the integration of differential privacy mechanisms with federated averaging algorithms, enabling multiple financial institutions to collaboratively train fraud-detection models without exposing sensitive customer data. Experimental evaluations on synthetic financial transaction datasets show that the framework achieves 94.7% detection accuracy under a configured differential privacy budget (ε = 1.0), with privacy accounting across training rounds as described in Section 3.3. By applying the combined sparsification and quantization strategy, the total communication volume decreases by 97.2% relative to the uncompressed baseline, while retaining 98.9% of the baseline accuracy (Table 3). This research provides practical guidance for financial institutions seeking to adopt privacy-preserving collaborative analytics that meet regulatory requirements, such as the Gramm-Leach-Bliley Act.