Modelling Default Risk of Borrowers: Evidence from Online Peer to Peer Lending Platforms in Australia

Abstract

Peer to Peer lending has the capacity to transforming the mass banking industry worldwide but credit risk modelling remains the core challenge of the platform. The general objective of this study is to analyse the credit default risk of borrowers of Peer to Peer online lending platform based in Australia. Specific objectives include the following;

1. To identify the loan information applicants provide to request for a loan facility,
2. Using RateSetter.com published data on loans to predict the likelihood of credit risk of the platform.

In this article, we employed binary logistic regression model to assess the likelihood of loan default. Based on the mathematical approach and the nature of dependent variable, we grouped variables into categorical, numerical-continuous as well as binary. The dependent variable is dichotomous whilst real-life dataset was retrieved from a popular and competitive online lending platform based in Australia from 2014-2017. We identified that early repayment, no mortgage tenant; car, debt consolidation, investment, major events, professional services, 3-year loan duration, 4-year loan duration, interest rate and income have significant influence on borrowers’ likelihood to default. Our empirical coefficients suggest that, there is 83.4% likelihood of borrowers default rate and hence recommended a critical examination of borrowers’ information presented to the platform. This paper fulfills the need to examine the credit information provided by loan applicants. Similarly, it endeavors to predict the possibility of borrowers default risk and the reasons contributing to online lending credit default risk.

Keywords: Credit Risk, Peer To Peer Online Lending, Binary Logistic Regression

DOI: 10.7176/RJFA/10-2-01