面向高维和不平衡数据的供应链金融信用评价

Credit evaluation of supply chain finance for high dimensional and unbalanced data

针对供应链金融模式下中小企业的信用风险控制问题,提出了一种面向高维和不平衡数据的信用风险预测模型。首先,基于Pearson-XGBoost两阶段特征选择建立供应链金融信用评价指标体系;其次,通过改进的NM-SMOTE算法对数据集进行平衡化;最后,利用Focal loss函数对XGBoost算法改进,并通过改进的粒子群算法进行优化,从而建立最终的信用评价模型。通过实验结果表明,提出的INS-IPSO-FLXGBoost模型对于中小企业具有更好的预测效果,可以更有效地识别风险企业。

Aiming at the credit risk control of small and medium-sized enterprises in the supply chain finance model,this paper proposed a credit risk prediction model for high dimensional and unbalanced data.Firstly,based on the Pearson-XGBoost two-stage feature selection,this model established the supply chain financial credit evaluation index system.Secondly,with the help of the improved NM-SMOTE algorithm,it made the dataset balanced.Finally,it used the Focal loss function to improve the XGBoost algorithm,and optimized it by the improved particle swarm algorithm,thus established the final credit evaluation mo-del.The experimental results show that the INS-IPSO-FLXGBoost model has a better prediction effect for small and medium-sized enterprises,and can identify risky enterprises more effectively.