Publication detail

The Predicting Power of Soft Information on Defaults in the Chinese P2P Lending Market

Author(s): dr. Ing. Zdeněk Drábek B.A., D.Phil., Zhengwei Wang
Msc Yao Wang PhDr, Zhengwei Wang
Type: IES Working Papers
Year: 2018
Number: 20
Published in: IES Working Papers 20/2018
Publishing place: Prague
Keywords: Soft Information, P2P Lending, Fintech, Microfinance, Credit Analysis, Empirical Study
JEL codes: D82; E51; G02;G14;G21:G23
Suggested Citation: Wang Y., Drabek Z. and Wang Z. (2018): "The Predicting Power of Soft Information on Defaults in the Chinese P2P Lending Market" IES Working Papers 20/2018. IES FSV. Charles University.
Abstract: Online peer to peer lending (P2P)– allows people who want to borrow money to submit their applications on the platform and individual investors can make bids on the loan listings. The quality of information in credit appraisal becomes paramount in this market. The existing research to assess the role of what is known as soft information in P2P markets has so far been very limited and, inconclusive due to differences in approaches and methodological limitations. The aim of the paper is to discuss the role of soft information channels in predicting defaults in the P2P lending market and to assess the importance of soft information in the Fintech companies’ credit analysis. Using a unique data of the Chinese P2P lending platform and new approach based on sets of hard and soft information, we compare the predicting performance of soft information, hard information and the combined role of both hard and soft information. We show that soft information can provide a valuable input in credit appraisal. The predicting power of soft information in our test was high, and together with hard information it can even help improve the loan performance. In exceptional situations characterized by the absence of hard financial data, soft information could be used, with caution, as an alternative.
Downloadable: wp_2018_20_wang




Patria Finance
Česká Spořitelna