Improving the management of microfinance institutions by using credit scoring models based on Statistical Learning techniques

Cubiles-de-la-Vega, María-Dolores; Blanco‐Oliver, Antonio; Pino‐Mejías, Rafael; Lara‐Rubio, Juan

doi:10.1016/j.eswa.2013.06.031

Cited by 38 publications

(19 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the widespread evidence highlighting the superiority of nonparametric models, with the latter usually proving to be more accurate credit‐scoring frameworks than classic statistical ones (Wilson & Sharda, ; Thomas, ; West, ; Benšić, Šarlija, & Zekić‐Sušac, ; Lee & Chen, ), it is widely surprising to discover that the development of credit‐scoring models in the microfinance industry by using nonparametric methodology has only minor advances. Blanco et al () and Cubiles‐De‐La‐Vega, Blanco‐Oliver, Pino‐Mejías, and Lara‐Rubio () are the two papers that propose a quantitative model of creditworthiness based on nonparametric models in MFI. Blanco et al .…”

Section: Data Variables and Methodologymentioning

confidence: 99%

“…In the same context, Cubiles‐De‐La‐Vega et al . () compared the performance of several credit‐scoring models: LDA and quadratic DA, LR, MLP, support vector machines, classification trees and ensemble methods based on bagging and boosting algorithms. They discovered that nonparametric approaches outperform logistic regression and LDA and quadratic DA.…”

Section: Data Variables and Methodologymentioning

confidence: 99%

“…According to West () and Abdou (), the relative ratio of misclassification costs associated with types I and II errors must be 1:5, and hence special attention should be paid to type II errors of all constructed models . To remedy this limitation and take into account the classification costs, different studies (Viganò, ; West, ; Abdou, 2009; Blanco et al ., ; Cubiles‐De‐La‐Vega et al ., ) have used the expected misclassification cost (EMC) criterion, defined as follows:

EMC = C (G | B) \times {italicπ}_{normalB} \times P (G | B) + C (B | G) \times {italicπ}_{normalG} \times P (B | G)

where C (G|B) and C (B|G) are the cost for misclassifying a bad loan as good (type II error) and the cost for misclassifying a good loan as bad (type I errors) respectively. P (G|B) and P (B|G) respectively measure the probabilities of type II and type I errors.…”

Section: Data Variables and Methodologymentioning

confidence: 99%

See 2 more Smart Citations

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

Baklouti

2014

Intell. Sys. Acc. Fin. Mgmt.

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: 99%

Section: Data Variables and Methodologymentioning

confidence: 99%

EMC = C (G | B) \times {italicπ}_{normalB} \times P (G | B) + C (B | G) \times {italicπ}_{normalG} \times P (B | G)

Section: Data Variables and Methodologymentioning

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.

View full text Add to dashboard Cite

show abstract

“…In this sense, Akkoç (), based on data from an international bank operating in Turkey, showed that an ANN improves the results obtained by parametric approaches (linear discriminant analysis, LR) but is slightly surpassed by a three‐stage hybrid adaptive neuro fuzzy inference system credit‐scoring model. Similarly, Cubiles‐de‐la‐Vega et al () compared data mining techniques with traditional methods. They found that an ANN obtains the best accuracy performance, above linear discriminant analysis, quadratic discriminant analysis, LR, support vector machines, classification and regression trees, bagged classification tree, random forest, adaptive boosting, binominal boosting and L 2 boosting.…”

Section: Extant Literaturementioning

confidence: 99%

Promoting Entrepreneurship at the Base of the Social Pyramid via Pricing Systems: A case Study

Lara‐Rubio

Blanco‐Oliver

Pino‐Mejías

2016

Intell. Sys. Acc. Fin. Mgmt.

View full text Add to dashboard Cite

Summary Historically, microfinance institutions (MFIs) have played a significant social role by helping people at the base of the socio‐economic pyramid escape from social exclusion through the creation of microenterprises. However, international banks have recently started competing in the microfinance sector. In this adverse environment, MFI management tools should be more innovative and technologically advanced to increase efficiency, solvency and profitability and to compete with commercial banks on equal terms. This study therefore strives to develop a credit‐risk management tool based on a multilayer perceptron (MLP) credit‐scoring model for a Peruvian MFI, and to calculate the capital requirements and microcredit pricing on both internal ratings‐based (IRB) and standardized approaches, analysing the impact of these models on the management of the MFI. Our findings show that the implementation of an IRB approach with default probabilities obtained from an MLP credit‐scoring model produces the best benefit by the MFIs in terms of higher accuracy (reduction of misclassification costs by 13.78%), lower capital requirements (in the range of 8.5–78%) and the best risk‐adjusted interest rates. Furthermore, with the establishment of interest rates adjusted to the real risk of each client, MFIs are fairer and more socially engaged by preventing economically viable low‐risk projects from becoming unviable due to excessive interest rates. This leads to the creation of more small businesses by people from the base of the socio‐economic pyramid and greater economic development and social cohesion. The IRB model should therefore be implemented to improve MFI solvency, profitability, efficiency, survival, management and social performance.

show abstract

“…They used four traditional statistical methods, multiple discriminant analysis (MDA), LR, neural networks (NN), and classification and regression trees (CART), and suggested that CART obtained the best accuracy performance. Cubiles-DeLa-Vega et al [6] used the credit card dataset of Peruvian microfinance institutions to develop credit prediction models 2 International Journal of Distributed Sensor Networks by using supervised classification techniques. Their proposed model exhibited better performance than LDA, LR, multilayer perceptron (MLP), and CART.…”

Section: Introductionmentioning

confidence: 99%

A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments

Bae

Kim

2015

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

This study suggests a methodology called a smart ubiquitous data mining (UDM) that consolidates homogeneous models in a smart ubiquitous computing environment. It tests the suggested model with financial datasets. It basically induces rules from the dataset using diverse rule extraction algorithms and combines the rules to build a metamodel. This paper builds several personal credit rating prediction models based on the UDM and benchmarks their performance against other models which employ logistic regression (LR), Bayesian style frequency matrix (BFM), multilayer perceptron (MLP), classification tree methods (C5.0), and neural network rule extraction (NR) algorithms. To verify the feasibility and effectiveness of UDM, personal credit data and personal loan data provided by a Financial Holding Company (FHC) were used in this study. Empirical results indicated that UDM outperforms other models such as LR, BFM, MLP, C5.0, and NR.

show abstract

Improving the management of microfinance institutions by using credit scoring models based on Statistical Learning techniques

Cited by 38 publications

References 23 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

Promoting Entrepreneurship at the Base of the Social Pyramid via Pricing Systems: A case Study

A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments

Contact Info

Product

Resources

About