What Determine Loan Rate and Default Status in Financial Technology

ABSTRACT: Using a large-scale dataset from three leading online peer-to-peer (P2P) lending platforms in Indonesia from 2014 to 2018, we investigate the determinants of platform interest rate and borrowers’ default status. Our result shows that loan and borrowers’ specific factors are significantly associated with the loan rate and loan default, although the relation could differ from one platform to another. Our empirical result shows that platforms focused on very small loan for microbusiness increase their interest rate after the introduction of formal regulation. It could be because of the increase of the borrowers requiring a very small amount of loan relatively much more than the number of lenders. The shortfall of supply then drives the increase in loan rate. Some policy implications are discussed.

Credit Risk in Online Peer-to-Peer Lending?.pdf

ABSTRACT: Effective assessment of borrower credit risk is the greatest challenge for peer-to-peer (P2P) lenders, especially in the Chinese market, where borrowers lack widely recognized credit scores. In this study, based on credit data from 2012 to 2015 from the website Renrendai.com, a logit model was used to assess borrower credit risk and predict the probability of default in every out-of-sample listing. The predicted probability of default was then compared with the actual default observation of default. The empirical results show that the logit model can evaluate the credit risk of P2P borrowers, and the model reduces the default rate to 9.5%, compared with the total sample default rate of 16.5%.

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