我国P2P网贷信用风险识别方式的实证研究

Empirical Study on the Credit Risk Identification of P2P Lending in China

信用风险识别是信贷的核心问题之一。近年来,P2P网贷异军突起,信用风险识别是其关键问题。本文以"人人贷"交易数据为样本,采用Logistic回归模型研究出借人的信用风险识别方式。研究表明,出借人分别从贷款特征、借款人基本特征与借款人信用特征等方面进行信用风险识别,借款人信用等级和是否有实地认证对出借人的投资意愿影响很大,贷款金额、期限与利率、满标次数、历史贷款表现和财务状况等贷款特征、借款人个体特征也具有重要影响。

Credit risk identification is one of the core issues of credit problems. In recent years, P2 P lending has sprung everywhere and credit risk identification is becoming the key point. This paper uses the binary Logistic regression model to study the Credit risk identification method of the investors based on the data of Ren Ren Dai. The research shows that when investors are identifying the credit risk, they will focus on the loan characteristics, the basic characteristics and the credit characteristics of the borrowers. Specifically, borrower＇s credit rating and the field certified indicator have enormous influence on the investment willingness while the loan characteristics such as the loan amount, duration, rate, successful bidding number, repayment performance, financial conditions and the individual characteristics of the borrowers also play an important role in the credit risk identification process.