Lasso-Cox模型在个人信用风险评估中的应用

借鉴Lasso模型和Cox模型的优点创建Lasso-Cox模型,用以评估个人借款者的信用风险,并利用个人借款者的数据进行实证分析,比较不同模型的预测精度。结果表明:(1)单一Cox模型的预测准确度高于Logistic回归模型,而Lasso-Cox模型的预测精度达95.76%,远高于单一Cox模型。(2)考虑宏观环境对个人经济的影响,发现将宏观变量纳入模型后预测精度有明显的提升,其中Lasso-Cox模型预测精度高达98.88%。

The Application of the Lasso-Cox Model in Personal Credit Risk Assessment

Based on the advantages of Lasso model and Cox model,this paper effectively established the Lasso-Cox model to evaluate the credit risk of individual borrowers and compare the prediction accuracy of different models by using individual borrower′s data.The results showed that:(1)The prediction accuracy of the single Cox model was higher than that of the Logistic regression model,and the prediction accuracy of the Lasso-Cox model was 95.76%,which was higher than that of the single Cox model.(2)Taking the impact of macro variables on the individual financial situation into account,the prediction accuracy was significantly improved when macro variables was included in the models,such as the Lasso-Cox model with the accuracy up to 98.88%.