Intelligent credit risk evaluation system using evolutionary-neuro-fuzzy scheme

Building an accurate credit scoring model is very important to predict effectively the creditworthiness of new customers. Neural networks and genetic algorithm are suitable for building highly predictive credit scoring model, but the lack of transparency of these methods is a major drawback. On the other hand the main advantage of fuzzy models is their ability to describe the behavior of systems with a series of linguistic humanly understandable rules. In this paper we develop an accurate as well as transparent credit scoring model based on the evolutionary-neuro-fuzzy method. Two datasets from the UCI Machine Learning Repository are selected to evaluate the proposed method.