Abstract
In P2P loans with information asymmetry, the text information described by the borrower plays an important role in alleviating the information asymmetry between borrowers and lenders. To explore the borrowing described in text information and its relationship with default behavior, this article selects credits from April 2014 to October 2016 as the repayment period and studies default data. This is performed based on the length of the excavated text, purpose of the loan, repayment ability, willingness to reimburse, five text variables, and degree of loan urgency. The empirical results show that text length has a significant negative correlation with the default probability of borrowers. Different loan purposes have different default risks. Interestingly, the more urgent a loan is, the more likely the borrower is to default. However, repayment ability information and repayment willingness information have no significant effect on default behavior. In addition, the Nagelkerke R2 improved by nearly 3% in the logistic regression model with the addition of text variables. In short, fully excavating loan description information is helpful in reducing the risk of loan default.
Highlights
With the continuous integration of finance and Internet technology, a new type of online lending model called peerto-peer online lending, which is a fast-lending model with no mortgage and no guarantee, has emerged [1,2,3]
For P2P lending platforms in China, borrowers usually provide the purpose of the loan, express the urgency of acquiring the loan, and describe their repayment intentions in the loan application. erefore, to test the influence of Chinese text length, loan purpose, repayment intention, and other information provided by the borrower on the probability of default, text analysis and the binary logistic regression were integrated to explore the relationship between loan description information and the influencing factors of default
2.532 0.233 0.542 0.333 0.414 0.764 0.993 1.035 1.419 1.279 1.698 1.235 text variables mined in this paper were included in the model. is shows that the text variable has a certain predictive effect on the default of the borrower and to some extent reflects that the description information of the loan can effectively reduce the information asymmetry between the lender and the borrower
Summary
With the continuous integration of finance and Internet technology, a new type of online lending model called peerto-peer online lending, which is a fast-lending model with no mortgage and no guarantee, has emerged [1,2,3]. Borrowers are required to provide the information required by the P2P platform, including basic personal information, financial information, and historical lending records, when applying for loans. Erefore, this paper will analyze the borrowing and loan default data of one of China’s most active P2P platforms (https://www.renrendai.com/) in depth and analyze the hidden information in the “soft information” with the borrower default behavior and the relationships between the designed variables to excavate the hidden “soft information” to effectively alleviate the information asymmetry between lenders and borrowers. Our results show that in-depth analysis of loan description information will help to reduce loan default risk. Security and Communication Networks supplements the influence of text length on the default risk in Chinese P2P platforms and (b) the paper provides a new test to assess whether the urgency of the borrower affects the probability of default.
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