Can Credit-Scoring Models Effectively Predict Microloans Default? Statistical Evidence from the Tunisian Microfinance Bank

Baklouti, Ibtissem; Bouri, Abdelfettah

doi:10.1142/9789814566148_0005

Cited by 2 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite their large‐scale application by the conventional banking sector, credit‐scoring models constitute an innovation for MFIs. Among the few credit‐scoring models developed for MFIs, most of them are predominantly based on traditional parametric statistical techniques, such as discriminant analysis (DA), logistic regression (LR) and probit regression (PR) (Viganò, ; Vogelgesang, ; Schreiner, ; Van Gool et al ., ; Baklouti & Bouri, ). Although these models are easy to implement and are able to generate straightforward results that can be readily interpreted, they suffer from many drawbacks.…”

Section: Data Variables and Methodologymentioning

confidence: 98%

“…Noteworthy, however, is that some authors believe it is difficult to incorporate and adapt this approach as an integral part of the credit decision process in the context of microfinance, for ignoring qualitative factors might well lead to inaccurate evaluations. These authors (Schreiner, ; Bumacov & Ashta, ; Van Gool et al ., ; Baklouti & Bouri, ) have reached the conclusion that credit‐scoring techniques can be incorporated into the microfinance area only as a complement, rather than a substitute, to the subjective judgmental approach. 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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Baklouti

2014

Intell. Sys. Acc. Fin. Mgmt.

Self Cite

View full text Add to dashboard Cite

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%.

show abstract

Section: Data Variables and Methodologymentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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

Baklouti

2014

Intell. Sys. Acc. Fin. Mgmt.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, the logistic regression method is the choice adopted for this study. This method is widely used in credit default studies where the dependent variable is binary Vallini et al (2008). This default is assessed in two stages.…”

Section: Methodsmentioning

confidence: 99%

“…The study also aims to support the borrowers in partner selection and group relationship management. In order to accomplish the research objective, this study aims at using the logistics regression method as many credits default studies have used it where the dependent variable is binary Vallini et al (2008). However, the logistics regression itself, as a traditional statistical method, suffers from relying on strong assumptions, such as the type of error distribution, additivity of the parameters within the linear predictor, and proportional hazards Rajula et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Kinship, gender and social links impact on micro group lending defaults

et al. 2023

View full text Add to dashboard Cite

Joint liability aspires to improve micro-loan performance through the support of, and pressure from, the group borrowers. This paper examines how the group composition, in terms of the mixture of kinship (Family) ties and social (Friends and Neighbours) ties among the borrowers, affects the default rates. Using binary logistics regression and three machine learning models, responses from 507 group micro-loan borrowers from four major Moroccan cities were analysed. The results show that the stronger the family and kinship ties are within a loan group, the higher is the default rate. On the contrary, the stronger the social ties are among the group, the lower is the default rate.Other key findings include that the diversion of fund usage from investment to consumption is not found to significantly cause default. Also, loan default can be a consequence of borrowers' strategic choice rather than financial distress.As compared to the female members, male borrowers are found to be causing higher default rates. Interestingly, the gender-related default rates are lower when more of the male borrowers are only socially related. Finally, group size is found to be positively associated with default. Our findings can help microfinance institutions refine their lending policies and guidance on groups' composition and size to reduce default rates.

show abstract

Can Credit-Scoring Models Effectively Predict Microloans Default? Statistical Evidence from the Tunisian Microfinance Bank

Cited by 2 publications

References 20 publications

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

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

Kinship, gender and social links impact on micro group lending defaults

Contact Info

Product

Resources

About