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E-Commerce Letters 2025
基于机器学习的商业银行绿色信贷风险管理研究
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Abstract:
本文通过构建基于大数据的综合评价体系，探讨提升绿色信贷风险评估效率和准确性的途径。选取了我国470家上市公司作为样本，运用SelectFromModel模型筛选关键指标，并结合SMOTE过采样算法处理样本不均衡问题。研究结果得出随机森林算法采用的机器学习模型更适用于评估绿色信贷风险。
This paper explores approaches to enhance the efficiency and accuracy of green credit risk assessment by constructing a comprehensive evaluation system based on big data. A sample of 470 listed companies in China was selected, with the SelectFromModel used to screen key indicators, and the SMOTE oversampling algorithm employed to address sample imbalance issues. The research findings indicate that the machine learning model utilizing the Random Forest algorithm is more suitable for assessing green credit risk.
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