Federated Early-Warning Modeling for Financial Credit Risk in Platform-Based Supply Chains

Abstract

Platform-based supply chains generate large volumes of financial, logistics, transaction, and behavioral data that can be used for credit risk early warning. However, data ownership, privacy restrictions, and institutional fragmentation often prevent centralized risk modeling. This article proposes a federated early-warning framework for financial credit risk assessment in platform-based supply chains. The framework enables banks, core enterprises, logistics providers, and digital platforms to collaboratively train credit risk models without directly sharing raw data. It integrates traditional credit indicators with machine learning features derived from transaction frequency, repayment patterns, inventory turnover, and supply-chain relationship stability. Explainable AI methods are used to interpret federated model outputs and improve trust among participating institutions. The framework is particularly suitable for small and medium-sized enterprises whose risk profiles cannot be fully captured by financial statements alone. The article contributes to supply-chain finance research by combining privacy-preserving learning, credit risk modeling, and explainable decision support.