Identification of Default Risks of Online Loan Borrowers:From the Perspective of Language Features in Loan Description

Abstract

Abstract: From the perspective of language features in loan description, this paper makes use of the loan data of online loan platform Company A in China from 2010 to 2018 to study how to effectively identify the default risks of online loan borrowers. The results show that such features as the degree of difficulty in reading the loan description language, the degree of positive language emotion and the length of the loan description language are positively correlated with the default risks of online loan borrowers, while the specific degree of the loan description language is negatively correlated with the default risks of online loan borrowers. The more difficult to read the loan description language is, the more positive the language emotion is, the longer the language length is, and the less specific the language is, the higher the default risks will be. According to the relationship between the characteristics of the loan description language and the default risks of the online loan borrowers, the online loan platforms can effectively identify and reduce the default risks of the online loan borrowers by optimizing the risk-assessment system, improving the risk-control model, and enriching the risk-control strategies.

Key words: online loan, default, risk identification, loan description, language characteristics

CLC Number:

F830.49

WANG San-xing, YUE Lei-lei, WEN Yan-bing, XIONG Ge. Identification of Default Risks of Online Loan Borrowers:From the Perspective of Language Features in Loan Description[J]. Contemporary Finance & Economics, 2020, 0(12): 63-74.

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