Antonio Blanco-Oliver

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

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 MFIs@? misclassification costs could be reduced to 13.7% with respect to the application of other classic models.

A Comparison of Classification/Regression Trees and Logistic Regression in Failure Models☆

Abstract The use of non-parametric statistical methods, the development of models geared towards the homogeneous characteristics of corporate sub-populations, and the introduction of non-financial variables, are three main issues analysed in this paper. This study compares the predictive performance of a non-parametric methodology, namelyClassification/Regression Trees (CART), against traditional logistic regression (LR) by employing a vast set of matched-pair accounts of the smallest enterprises, known as micro-entities,from the United Kingdom for the period 1999 to 2008 that includes financial, non-financial, and macroeconomic variables. Our findings show that CART outperforms the standard approach in the literature, LR.

Promoting Entrepreneurship at the Base of the Social Pyramid via Pricing Systems: A case Study

Historically, microfinance institutions MFIs have played a significant social role by helping people at the base of the socio-economic pyramid escape from social exclusion through the creation of microenterprises. However, international banks have recently started competing in the microfinance sector. In this adverse environment, MFI management tools should be more innovative and technologically advanced to increase efficiency, solvency and profitability and to compete with commercial banks on equal terms. This study therefore strives to develop a credit-risk management tool based on a multilayer perceptron MLP credit-scoring model for a Peruvian MFI, and to calculate the capital requirements and microcredit pricing on both internal ratings-based IRB and standardized approaches, analysing the impact of these models on the management of the MFI. Our findings show that the implementation of an IRB approach with default probabilities obtained from an MLP credit-scoring model produces the best benefit by the MFIs in terms of higher accuracy reduction of misclassification costs by 13.78%, lower capital requirements in the range of 8.5-78% and the best risk-adjusted interest rates. Furthermore, with the establishment of interest rates adjusted to the real risk of each client, MFIs are fairer and more socially engaged by preventing economically viable low-risk projects from becoming unviable due to excessive interest rates. This leads to the creation of more small businesses by people from the base of the socio-economic pyramid and greater economic development and social cohesion. The IRB model should therefore be implemented to improve MFI solvency, profitability, efficiency, survival, management and social performance.