The loan officer's subjective judgment and its role in microfinance institutions

Baklouti, Ibtissem; Bouri, Abdelfattah

doi:10.1504/ijram.2014.062778

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“…They have advanced that the loan officer's exclusion from the microfinance activity seems so far an impossible undertaking, since a high share of risk in microcredit is usually linked with qualitative information pertaining to the entrepreneur's personality and ability to run a business. In this respect, a few researchers (Cornée, ; Baklouti & Bouri, ) have responded by developing a credit‐scoring model using both hard and soft information. Soft information denotes the loan officer's intuitions, impressions or feelings regarding an entrepreneur's management capacity.…”

Section: Introductionmentioning

confidence: 99%

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

Baklouti

2014

Intell. Sys. Acc. Fin. Mgmt.

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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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Section: Introductionmentioning

confidence: 99%