Federated Learning for Privacy-Preserving Financial Modeling across Institutions
Keywords:
federated learning, financial modeling, privacy, governance, model risk, secure aggregation, differential privacy, operational resilienceAbstract
Financial institutions increasingly seek to improve predictive models for credit risk, fraud detection, anti-money laundering, and customer behavior by combining evidence distributed across organizational boundaries. Direct data sharing is often prohibited by privacy regulation, commercial confidentiality, and security concerns. Federated learning has emerged as a compelling systems paradigm that permits collaborative model training without centralizing raw client records. This paper provides a system-level examination of federated learning for privacy-preserving financial modeling across institutions. It develops a critical analysis of architectural choices, trust governance, deployment sustainability, robustness, fairness, and regulatory alignment. Rather than focusing on algorithmic details, the discussion emphasizes structural trade-offs among centralized orchestration, decentralized coordination, heterogeneous data distributions, and institutional risk appetite. The paper examines how federated financial systems can be designed to preserve auditability, prevent model poisoning, manage differential privacy budgets, and align with operational resilience expectations. Cross-domain comparisons with mobile and medical federated deployments are used to illuminate transferable lessons and sector-specific constraints. The analysis further considers the role of confidential computing, secure aggregation, entity resolution, and distributed ledger technologies in building credible institutional governance. Policy implications are discussed in relation to data protection law, model risk management, cross-border data flow, and emerging artificial intelligence regulation. The paper concludes that federated learning for financial modeling is best understood as a socio-technical infrastructure challenge in which machine learning performance must be jointly optimized with institutional trust, legal defensibility, and long-term operational viability.
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