Using Bivariate Probit Model to Analyze Bank Lending Policy

This paper builds on the credit-scoring literature and proposes a method to evaluate the bank lending policy. We use the bivariate probit approach to estimate an unbiased scoring model. The data set with large commercial loans data provided by a commercial bank of China to estimate the model contains some financial and firm information on both rejected and approved applicants. In the bivariate probit model, we find a significant cross equation between loans rejected and loans granted. That means only uses loan granted data to develop a credit-scoring model will suffer from a sample-selection bias. Based on our unbiased results, the interest rate and other key variables have positive (negative) effects on the probability of granting a loan also have the same effect on the probability of default, implying that the bank adopt a lending policy prefers to risk minimization but not profit maximization