Credit scoring models for the microfinance industry using neural networks: Evidence from Peru

Authors:
Antonio Blanco;Rafael Pino-MejíAs;Juan Lara;Salvador Rayo
Affiliations:
Department of Financial Economics and Operations Management, Faculty of Economics and Business Studies, University of Seville, Avda. Ramon y Cajal, 1, 41018 Seville, Spain;Department of Statistics and Operational Research, Faculty of Mathematics, University of Seville, Avda. Reina Mercedes, s/n 41012 Seville, Spain;Department of Financial Economics and Accounting, Faculty of Economics and Business Studies, University of Granada, Campus Cartuja, s/n 18071 Granada, Spain;Department of Financial Economics and Accounting, Faculty of Economics and Business Studies, University of Granada, Campus Cartuja, s/n 18071 Granada, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

Credit scoring systems are currently in common use by numerous financial institutions worldwide. However, credit scoring with the microfinance industry is a relatively recent application, and no model which employs a non-parametric statistical technique has yet, to the best of our knowledge, been published. This lack is surprising since the implementation of credit scoring should contribute towards the efficiency of microfinance institutions, thereby improving their competitiveness in an increasingly constrained environment. This paper builds several non-parametric credit scoring models based on the multilayer perceptron approach (MLP) and benchmarks their performance against other models which employ the traditional linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR) techniques. Based on a sample of almost 5500 borrowers from a Peruvian microfinance institution, the results reveal that neural network models outperform the other three classic techniques both in terms of area under the receiver-operating characteristic curve (AUC) and as misclassification costs.