基于BO-XGBoost与集成学习方法的供应链金融信用评价研究

针对供应链金融领域中小企业融资的信用风险控制问题，提出了一种在Bagging算法框架下结合贝叶斯优化和XGBoost算法的集成学习模型BO-XGBoost-Bagging（BXB）。首先，基于XGBoost特征重要度进行特征筛选，建立供应链金融信用评价指标体系。其次，通过贝叶斯优化获得XGBoost的最优超参数，并结合Bagging算法得到集成模型BXB。最后，在中小企业数据集上进行预测，通过实证研究验证信用评价模型的有效性。实证结果表明，BXB模型相比其他模型具有更好的预测效果，能够更加准确、全面地对中小企业的信用风险进行评估，更好地区分风险企业和正常企业，最大程度减少违约损失，在供应链金融信用评价方面有着较高的应用价值。

Credit evaluation of supply chain finance based on BO-XGBoost and ensemble learning method

To solve the problem of the credit risk control of the small and medium-sized enterprises(SMEs) financing in the supply chain finance field, an ensemble learning model BO-XGBoost-Bagging (BXB) that combines Bayesian optimization and XGBoost was proposed under the Bagging framework. Firstly, based on the XGBoost feature importance, the feature screening was carried out, and the financial credit evaluation index system for the supply chain was established. Secondly, the optimal super parameters of XGBoost were obtained by Bayesian optimization, and the integrated model BXB was obtained by bagging. Finally, the prediction was performed on SMEs data set, and the effectiveness of the credit evaluation model was verified by empirical research. The empirical results show that the BXB model has a better predictive effect than other models and can evaluate the credit risk of SMEs more exactly and comprehensively. The model can better distinguish between risky companies and normal companies, and minimize default losses. It has high application value in the credit evaluation of supply chain finance.