Prepayment and Default of Consumer Loans in Online Lending

Online lending provides a means of fast financing for borrowers based on their creditworthiness. However, borrowers may undermine this agreement due to early repayment or default, which are two major concerns for the platform and lenders, since both affect the profitability of a loan. While default risk is frequently focused on in credit scoring literature, prepayment has received much less attention, despite a higher prepayment rate being observed in online lending when compared with default. This paper uses multivariate logistic regression to predict the probability of both the underlying prepayment and default risk. Real consumer lending data of 160,956 unsecured loans provides evidence that these two events have their own distinct patterns. The model, incorporating macroeconomic factors, also addresses the influence of economic conditions which cannot be ignored since the last financial crisis. The out-of-sample prediction results have shown that both prepayment and default can be accurately predicted