摘要
文以当前银行信贷业务中客户违约问题为出发点,将客户违约率和信贷评分卡分值的关系合理映射。运用逻辑回归建立评分卡预测模型,使用梯度下降算法来实现银行风险投资中客户评分卡的构建。首先加载数据并对数据进行分析,接着划分数据集,并使用跨时间验证集作为模型最后的验证。最后使用KS值和AOC曲线双向评价模型的稳定性。实验证明,采用所提方法构建的评分卡模型具有较好的稳定性。
This paper takes the problem of customer default in the current bank credit business as the starting point and maps the relationship between customer default rate and credit score card value reasonably.The logistic regression is used to build the prediction model of the score card and the gradient descent algorithm is used to construct the customer score card in the bank venture capital.The data is first loaded and analyzed,then the data set is partitioned and the cross-time validation set is used as the final validation of the model.Finally,KS value and AOC curve are used to evaluate the stability of the model.Experimental results show that the score card model constructed by the proposed method has good stability.
作者
边玉宁
陆利坤
李业丽
曾庆涛
孙彦雄
BIAN Yu-ning;LU Li-kun;LI Ye-li;ZENG Qing-tao;SUN Yan-xiong(Beijing Institute of Graphic Communication,Beijing 102600,China)
出处
《计算机科学》
CSCD
北大核心
2020年第S02期116-118,共3页
Computer Science
基金
北京科技创新服务能力建设项目(PXM2016_014223_000025)
广东省科技重大专项项目(190826175545233)。
关键词
金融风投
逻辑回归
机器学习
评分卡
Financial venture capital
Logistic regression
Machine learning
Score card