The predicting power of soft information on defaults in the Chinese P2P lending market - Yao Wang; Zdenek Drabek; Zhengwei Wang (Charles University in Prague, Institute of Economic Studies (IES), 2018)
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 RRDai.com 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.
Keywords: D82; E51; G02; G14; G21; G23; 330; Soft Information; P2P Lending; Fintech; Microfinance; Credit Analysis; Empirical Study