A Comparative Assessment of Credit Risk Model Based on Machine Learning ——a case study of bank loan data

Abstract

Recently some techniques (such as statistical techniques and machine learning techniques) have been developed for evaluating individual credit information to decide whether the person meets the criteria of credit financing, and the process is known as credit scoring. This paper mainly focuses on the comparative assessment of the performances of five popular classifiers involved in machine learning used for credit scoring: Naive Bayesian Model, Logistic Regression Analysis, Random Forest, Decision Tree, and K-Nearest Neighbor Classifier. Each classifier has its own strength and weakness, it is assertive to say which one is the best. However, the results of this experiment pinpoint that Random Forest performs better than others in terms of precision, recall, AUC (area under curve) and accuracy.