Evaluating Various Machine Learning Techniques in Credit Risk Area

Abstract

Implementing machine learning techniques to credit scoring is a popular method, which is widely used by many financial institutions and banks at present. As the fast development of machine learning tools, these technologies could provide people more accurate predictions and help enterprises avoid future risk. A supervised machine learning technique is utilized in this research as the classification approach. In this experiment, several machine learning algorithms will be compared in order to present the performance by evaluating the type of credit risk. The data is about assessing customers of a German banking systems from the UCI Machine Learning Repository, which contains 5000 instances and 21 attributes. The final result of this research shows the comparison of 12 scenarios among different combinations of balancing methods, feature selection methods, and predictive algorithms, which finally presents that the collection of Adaptive Synthetic, Boruta and k-Nearest Neighbor receives the highest accuracy score.