A Neural Network Approach for Microfinance Credit Scoring

Credit scoring has been successfully applied in domains as mortgage loans and credit scorecards. However, credit scoring with the microfinance industry is a relatively recent application. Most of the financial and banking institutions are using logistic regression to build a credit scorecard. Among the new method artificial neural networks have been applied in various studies of scorecard modelling. This paper analyzes comparative descriptions of both the artificial neural network and logistic regression model in credit scoring for microfinance data. The theoretical features and properties, which include parameters, variable selection, and model evaluation, followed by comparisons of the advantages and disadvantages of both the models are analytically reviewed.