Capital Flow Risk Modeling and Empirical Analysis of Financial Data From The Perspective of Supply Chain Finance

Supply chain finance, a key tool for optimizing capital allocation across the industrial chain, faces core risks centered on disruptions and blockages in capital flows. This study focuses on capital flow risk in the context of supply chain finance, constructing a multi-level risk identification and quantitative assessment model and conducting empirical testing based on enterprise financial data. The study first systematically examines the formation mechanisms and transmission pathways of supply chain capital flow risk, identifying key dimensions such as core enterprise credit risk, trade background authenticity risk, operational risk, and external environmental risk. Furthermore, combined with enterprise financial statement data, a comprehensive capital flow risk assessment model integrating logistic regression with an indicator-based early warning system is constructed. The empirical analysis utilizes the financial data of 500 sample enterprises across the upstream, midstream, and downstream supply chains in my country's manufacturing and wholesale and retail sectors over the past five years, along with selected supply chain finance business data, which has been rigorously cleansed and processed. The results demonstrate that the quick ratio and cash flow gap volatility of core enterprises significantly explain the overall supply chain capital flow risk; abnormally prolonged accounts receivable turnover days for small and medium-sized enterprises is an important early warning signal of risk; the model achieves an 82.3% accuracy rate in identifying high-risk enterprises, with an average early warning time of three months. This study provides financial institutions with operational model tools and data support for accurately identifying, quantifying and managing supply chain finance cash flow risks, and has practical value for improving the robustness and efficiency of supply chain financial services.