Intelligent credit scoring model using soft computing approach

This paper proposes an intelligent credit scoring model using a hybrid soft computing method. The aim of this method is to extract credit scoring models from data that not only have the required performance, but is also relatively interpretable, which is very important to predict effectively the creditworthiness of the new customers and to understand the decision process of the model. To achieve these two objectives: accuracy and interpretability, an evolutionary-neurofuzzy method is adopted. In the first phase, a fuzzy rule base is automatically extracted from a data set using a clustering method, then genetic algorithm is used to increase the performance of the fuzzy inference system in the second phase. In the last phase a multi-objective genetic algorithm is applied to achieve two goals: to preserve the accuracy of the fuzzy model to a given value and to enhance the interpretability of the fuzzy model by reducing the fuzzy sets in the rule base. Two datasets from the UCI Machine Learning Repository are selected to evaluate the proposed method.