A Credit Scoring Model for Microfinance Bank Based on Fuzzy Classifier Optimized by a Differential Evolution Algorithm

The process of effective credit risk assessment plays an important role in the financial decision making in Microfinance Institutions (MFIs) as it enables faster credit approval decisions and diminishes the possible risks associated with customers' repayment defaults. Credit scoring is the most commonly used technique for evaluating the creditworthiness of loans which has gradually begun to find its way into the microfinance field. Many parametric and nonparametric techniques have been adopted by financial institutions to develop accurate credit scoring models. In this study, a credit scoring model is developed for a Tunisian Microfinance Bank by applying fuzzy classifiers where the fuzzy knowledge bases are optimized through differential evolution. Further, the performance of the proposed model is compared to that of the decision tree model. The obtained results reveal that the proposed model consistently gives a better average correct classification rate than the decision tree model. As with the decision tree model, the proposed model can be easily understood by any user and is very useful in the context of credit evaluation process, since it is in ‘if-then' rule form; unlike decision tree model, the proposed model does not stay in a black box. In the proposed model, the interpretation of independent variables may provide valuable information for bankers and consumers, especially in explaining why credit applications are rejected