A Psychological Approach to Microfinance Credit Scoring via a Classification and Regression Tree

Abstract

Microfinance institutions' (MFIs') peculiar lending methodology is characterized by an unchallenged decisionmaking predominance from the part of loan officers. Indeed, the latter are in charge of providing a great deal of diagnostic information regarding the entrepreneur's psychological traits likely to help them run a business. This paper constitutes an initial attempt towards exploring the role of borrowers' psychological traits in predicting future default occurrences. It builds on a nonparametric credit scoring model, based on a decision tree, including borrowers' quantitative behavioural traits as input for the final scoring model. On applying data collected from a Tunisian microfinance bank, the major depicted result lies in the fact that borrowers' psychological traits constitute a major information source in predicting their creditworthiness. Actually, the variables deployed have helped reduce the proportion of bad loans classified as good loans by 3.125%, which leads to a decrease in MFIs' losses by 4.8%. In addition, the results indicate that the scoring model based on a classification and regression tree (CART) outperforms the classic techniques. Actually, implementing this CART model might well help MFIs reduce misclassification costs by 6.8% and 13.5% in comparison with the discriminant analysis and logistic regression models respectively. Our conceived model, we consider, would be of great practical implication for microfinance and may provide a means for securing competitive advantage over other MFIs that fail to implement such a methodology.1 These quantitative criteria are gathered from the loan application form. Usually, the loan officers collect and verify the personal identification of a borrower (identity card), their educational level and their professional experience. 2 Sociologists define social capital as the social networks, norms, and cooperation and trust created by human interactions in a community (e.g. see Putnam (1995)). 3 Basically, quantitative information is objective; qualitative is subjective. b AUC(1) and AUC(2) are statistically significant at the threshold of 1%.

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