Federated and Privacy-Preserving Machine Learning for Uncertainty-Aware Credit Default Risk Prediction in Distributed Financial Systems

Abstract

Credit-default prediction is an important function of modern financial institutions because inaccurate assessment of borrower risk can increase loan losses, capital requirements, and systemic exposure. The growing availability of financial data has created opportunities for machine-learning-based credit-risk prediction, but centralized data architectures introduce significant privacy, security, and governance challenges. Financial institutions frequently maintain borrower information in separate databases because of regulatory restrictions, commercial confidentiality, and institutional policies. Federated learning provides an alternative architecture by enabling machine-learning models to be trained across distributed datasets without requiring the underlying data to be transferred to a central repository. However, distributed prediction also requires mechanisms for evaluating the reliability and uncertainty of model outputs. This study examines federated and privacy-preserving machine learning for uncertainty-aware credit-default risk prediction in distributed financial systems. A conceptual literature-based methodology was employed to synthesize research on multivariate financial-risk prediction, ensemble learning, federated credit-risk assessment, homomorphic encryption, privacy-preserving learning, and probabilistic AI diagnostics. Mahajan et al. (2024) demonstrate the relevance of multivariate data analytics and ensemble learning to financial-risk prediction. Konatham et al. (2025) extend this perspective to distributed credit-risk assessment through federated learning and homomorphic encryption. Barvaliya (2025) contributes a probabilistic-AI perspective through convergence assurance and rank-aware, tail-sensitive diagnostics for multi-chain inference. The findings indicate that an integrated architecture can combine local financial-risk modelling, federated learning, cryptographic protection, and uncertainty diagnostics. The proposed framework retains sensitive borrower information within institutional environments while supporting collaborative model development and reliability-aware credit-risk prediction. The study identifies data heterogeneity, class imbalance, communication overhead, privacy-utility trade-offs, computational cost, model security, and uncertainty assessment as important challenges for distributed financial machine learning.