Credit rating of online lending borrowers using recovery rates

Rongda Chen, Xinhao Chen, Chenglu Jin, Yiyang Chen and Jiayi Chen

International Review of Economics & Finance, 2020, vol. 68, issue C, 204-216

Abstract: A core issue of the rapid developing online lending is to establish a sound credit rating method for borrowers. When examining 14,558 recovery rates of defaulted assets corresponding to each borrower of Renrendai platform from 2011 to 2016, we find that the current credit rating system (seven levels from AA, A, B, C, D, E and HR) cannot distinguish the distribution of recovery rates. Accordingly, this study proposes a credit rating approach for online lending platform using a Chinese sample, given that online lending has developed far more in China than in other countries and regions. Firstly, by referencing to the Sesame Credit and US FICO Credit systems, 15 indices are selected. Secondly, the K-Means clustering credit rating method using recovery rate is used to reclassify borrowers, solving the problem that Renrendai’s credit rating system cannot distinguish borrowers with assets of different recovery rates. However, this simple reclassification lead to another issue that borrowers with high credit rating are more than borrowers with low credit rating (the so-called "inverted pyramid" problem). Therefore, finally, an augmented credit rating method is developed to reclassify borrowers, which integrates factor analysis and K-Means clustering using recovery rate. By using this finalized method, borrowers with different recovery rates are distinguished clearly to different credit levels.

Keywords: Online lending; Credit rating approach; Recovery rates; K-Means clustering (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:68:y:2020:i:c:p:204-216

DOI: 10.1016/j.iref.2020.04.003

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