Improving the management of microfinance institutions by using credit scoring models based on Statistical Learning techniques

• Statistical Learning methods outperform logistic regression and linear and quadratic discriminant analysis.
• Credit scoring models based on supervised classification algorithms increase the efficiency of microfinance institutions.
• With the implementation of a MLP-based model, the MFIś misclassification costs are reduced to 13.7%.
• A freely available software, the R system, can be used to fit these models.

A wide range of supervised classification algorithms have been successfully applied for credit scoring in non-microfinance environments according to recent literature. However, credit scoring in the microfinance industry is a relatively recent application, and current research is based, to the best of our knowledge, on classical statistical methods. This lack is surprising since the implementation of credit scoring based on supervised classification algorithms should contribute towards the efficiency of microfinance institutions, thereby improving their competitiveness in an increasingly constrained environment. This paper explores an extensive list of Statistical Learning techniques as microfinance credit scoring tools from an empirical viewpoint. A data set of microcredits belonging to a Peruvian Microfinance Institution is considered, and the following models are applied to decide between default and non-default credits: linear and quadratic discriminant analysis, logistic regression, multilayer perceptron, support vector machines, classification trees, and ensemble methods based on bagging and boosting algorithm. The obtained results suggest the use of a multilayer perceptron trained in the R statistical system with a second order algorithm. Moreover, our findings show that, with the implementation of this MLP-based model, the MFIś misclassification costs could be reduced to 13.7% with respect to the application of other classic models.