论文标题
基于分类的信用风险分析：贷款俱乐部的情况
Classification based credit risk analysis: The case of Lending Club
论文作者
论文摘要
在本文中，我们对一家名为Lending Club的公司的过去贷款申请人的数据进行了信用风险分析。计算需要使用探索性数据分析和机器学习分类算法，即逻辑回归和随机森林算法。我们进一步使用了默认的计算概率来根据信用默认交换的想法设计信用导数，以对冲违约事件。测试集上的结果使用各种绩效指标提出。
In this paper, we performs a credit risk analysis, on the data of past loan applicants of a company named Lending Club. The calculation required the use of exploratory data analysis and machine learning classification algorithms, namely, Logistic Regression and Random Forest Algorithm. We further used the calculated probability of default to design a credit derivative based on the idea of a Credit Default Swap, to hedge against an event of default. The results on the test set are presented using various performance measures.
