In the context of accelerated globalization, supply chain finance in the United States, as a key means to alleviate the financing difficulties of small and medium-sized enterprises, is facing dual challenges of systemic risks and supply chain stability. The COVID-19 pandemic and geopolitical factors in 2020 led to a 32% surge in bankruptcy rates for small and medium-sized enterprises, while currently 40% of small and medium-sized enterprise loans in the United States are pledged through the supply chain system (FDIC, 2023). Traditional credit evaluation models that rely on financial data and subjective judgments are no longer able to capture the complex risk characteristics of supply chain finance[1-3]. The breakthrough in artificial intelligence technology has brought innovation to this field: the credit evaluation framework based on LightGBM has reduced the default probability of photovoltaic companies' supply chain by 23% (p<0.05) in the Federal Reserve's stress test, achieving a 1.8-fold improvement in evaluation accuracy and a 27% decrease in misjudgment rate compared to traditional methods, directly responding to the Federal Reserve's policy orientation of strengthening systemic risk management through regulatory technology (RegTech) in 2024. The core advantage of this model lies in capturing nonlinear features[4-5], such as quantifying the 21.3% contribution of federal subsidy policies to credit risk and the 28.7% risk weight of operational indicators such as supplier concentration. This dynamic analysis has been validated in empirical studies of photovoltaic companies such as First Solar.On the technical level, natural language processing integrates unstructured data such as technology investment to construct dynamic models[6-7], LSTM neural networks effectively capture the nonlinear effects of external shocks such as geopolitics and epidemics, and smart contracts reduce operational risks by automating the execution of financing terms. The policy side establishes an encrypted anonymization protocol for cross institutional credit data collaboration through the 2024 Data Privacy and Security Act (16 CFR § 314.5), the technical side optimizes resource allocation and enhances supply chain resilience[8-10], and the practical side provides data-driven decision support for enterprises and financial institutions[11-13]. In the future, the integration of ESG data, NLP analysis of corporate annual report strategic information, and the fusion of blockchain and AI technologies through deep learning will promote the transformation of supply chain finance towards full chain risk management. This innovation not only provides technical support for the United States to address systemic risks, but also helps enhance the competitiveness of the industrial chain through standardized data anonymization mechanisms (such as CCPA) and blockchain tax incentive policies, forming a positive interaction between policies, technology, and industry[14-16].

Yuchen Huang, Xuezhi Wu. Risk Assessment and Governance of Supply Chain Finance Driven By AI: Research on Improving National Financial Stability and Technological Governance Efficiency. International Journal of Business Management and Economics and Trade (2025), Vol. 6, Issue 1: 115-125. https://doi.org/10.38007/IJBMET.2025.060112.

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