<p>This study focuses on the complexity and challenges of risk assessment in Supply Chain Finance (SCF) from an Environmental, Social and Governance (ESG) perspective. Through in-depth analysis of interpretable machine learning models, their effectiveness in identifying ESG-related risk was evaluated. Firstly, a SCF risk assessment framework that integrated ESG factors was designed. Subsequently, the data were preprocessed using the synthetic minority oversampling technique (SMOTE). Then, a variety of machine learning models for risk assessment were applied. Finally, through ablation experiment and Shapley Additive interpretation (SHAP) of XGBoost (Extreme Gradient Boosting), this study explained the contribution and importance of each risk factor to the results. This study effectively solved the interpretability problem of black-box machine learning model. The results of ablation experiments showed that ESG factors had certain influence on the risk of SCF. In addition, SHAP method further highlighted the core role of asset-liability ratio, cash ratio and quick ratio in risk assessment. This study revealed the practical application effects of different models in financial risk assessment, and used SHAP algorithm to provide clearer verification of machine learning results. It filled the gap in interpretable machine learning in SCF field. And it also provided strong support for promoting green and sustainable supply chain financial resources.</p>