A New Machine Learning Method for Bank Credit Risk Analysis

Abstract

We present in this article a new approach to predict the creditworthiness of borrowers that we call “Method of separating the learning set into two regions”. The goal of this approach is to build two regions from a training set. Thus, to predict the solvency of borrowers, it suffices to identify which of the two regions has its characteristic vectors; if it does not correspond to any region, credit decision-making requires further analysis. To test our approach, a large set of real and recent credit data obtained from the UCI repository is used, we trained also on a real credit database of a Moroccan bank and the creditworthiness of borrowers is analyzed at using two performance measurement indicators such as classification accuracy and AUC of the ROC curve as a robustness measurement criterion. The proposed model was compared with three traditional machine learning algorithms: LR, RBF-NN and MLP-NN. The experimental results show the superiority of the proposed approach.