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

作者：

Highlights：

• Statistical Learning methods outperform logistic regression and linear and quadratic discriminant analysis.

• Credit scoring models based on supervised classification algorithms increase the efficiency of microfinance institutions.

• With the implementation of a MLP-based model, the MFIś misclassification costs are reduced to 13.7%.

• A freely available software, the R system, can be used to fit these models.

摘要

•Statistical Learning methods outperform logistic regression and linear and quadratic discriminant analysis.•Credit scoring models based on supervised classification algorithms increase the efficiency of microfinance institutions.•With the implementation of a MLP-based model, the MFIś misclassification costs are reduced to 13.7%.•A freely available software, the R system, can be used to fit these models.

论文关键词：Decision support systems,Microfinance institutions,Credit scoring,Efficiency,Statistical Learning,Data mining

论文评审过程：Available online 29 June 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.06.031