论文标题
通过极端梯度提高预测银行贷款违约
Predicting Bank Loan Default with Extreme Gradient Boosting
论文作者
论文摘要
贷款违约预测是银行和其他金融机构面临的最重要，最关键的问题之一，因为它对利润产生了巨大影响。尽管存在许多传统方法用于挖掘有关贷款申请的信息，但大多数这些方法似乎表现不佳，因为据报道不良贷款数量增加。在本文中，我们使用一种称为XGBoost的极端梯度增强算法进行贷款默认预测。该预测基于来自领先银行的贷款数据，考虑了贷款申请和申请人人口的数据集。我们还提出了重要的评估指标，例如分析的准确性，回忆，精度，F1得分和ROC区域。本文为贷款信贷批准提供了有效的基础，以便使用预测建模从大量贷款申请中确定风险的客户。
Loan default prediction is one of the most important and critical problems faced by banks and other financial institutions as it has a huge effect on profit. Although many traditional methods exist for mining information about a loan application, most of these methods seem to be under-performing as there have been reported increases in the number of bad loans. In this paper, we use an Extreme Gradient Boosting algorithm called XGBoost for loan default prediction. The prediction is based on a loan data from a leading bank taking into consideration data sets from both the loan application and the demographic of the applicant. We also present important evaluation metrics such as Accuracy, Recall, precision, F1-Score and ROC area of the analysis. This paper provides an effective basis for loan credit approval in order to identify risky customers from a large number of loan applications using predictive modeling.
