LAUSR

Abstract Credit scoring model development is very important for the lending decisions of financial institutions. The creditworthiness of borrowers is evaluated by assessing their hard and soft information. However, microfinance borrowers are very sensitive to a local economic downturn and extreme (weather or climate) events. Therefore, this paper is devoted to extending the standard credit scoring models by taking into account the spatial dependence in credit risk. We estimate a credit scoring model with spatial random effects using the distance matrix based on the borrowers’ locations. We find that including the spatial random effects improves the ability to predict defaults and non-defaults of both individual and group loans. Furthermore, we find that several loan characteristics and demographic information are important determinants of individual loan default but not group loans. Our study provides valuable insights for professionals and academics in credit scoring for microfinance and rural finance.