A Hybrid Machine Learning Model for Credit Approval

Abstract

Incorrect decision-making in financial institutions is very likely to cause financial crises. In recent years, many studies have demonstrated that artificial intelligence techniques can be used as alternative methods for credit scoring. Previous studies showed that prediction models built using hybrid approaches perform better than single approaches. In addition, feature selection or instance selection techniques should be incorporated into building prediction models to improve the prediction performance. In this study, we integrate feature selection, instance selection, and decision tree techniques to propose a new approach to predicting credit approval. Experimental results obtained using the survey data show that our proposed approach is superior to the other five traditional machine learning approaches in the measures. In addition, our approach has a lower cost effect than the traditional five methods. That is, the proposed approach generates fewer costs, such as money loss, than the traditional five approaches.