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Microfinance institutions' (MFIs') peculiar lending methodology is characterized by an unchallenged decision-making 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
