论文标题
确定P2P贷款中信用评估的次要属性
Determining Secondary Attributes for Credit Evaluation in P2P Lending
论文作者
论文摘要
传统银行组织以及点对点贷款实体的二级信用评估方法的需求越来越大。这在当今的技术时代尤其重要，在当今的技术时代，坚持严格的原始信用历史并不有助于区分“好”和“坏”借款人，最终会伤害个人借款人以及整个投资者。我们利用机器学习分类和聚类算法来准确预测借款人的信誉，同时确定有助于该分数的特定次要属性。尽管在预测何时全额支付贷款方面进行了广泛的研究，但贷款特征选择的领域相对较新。在识别关键次要属性的同时，我们在LendingClub数据上获得了65％的F1和73％的AUC。
There has been an increased need for secondary means of credit evaluation by both traditional banking organizations as well as peer-to-peer lending entities. This is especially important in the present technological era where sticking with strict primary credit histories doesn't help distinguish between a 'good' and a 'bad' borrower, and ends up hurting both the individual borrower as well as the investor as a whole. We utilized machine learning classification and clustering algorithms to accurately predict a borrower's creditworthiness while identifying specific secondary attributes that contribute to this score. While extensive research has been done in predicting when a loan would be fully paid, the area of feature selection for lending is relatively new. We achieved 65% F1 and 73% AUC on the LendingClub data while identifying key secondary attributes.
