Credit Card Risk Assessment Based on Machine Learning

Abstract

Our credit card business has developed a rapidly rising stage after a slow market incubation period. The rapid development of the credit card business on one hand led to the rapid development of Chinese economy, on the other hand, however, it brought the bank a large dose of credit risk. The number of credit cards issued by commercial banks is increasing. Customer credits risk issues receiver more attention. Credit rating has become an important technical means for banks to improve their risk management. This paper proposes a machine learning technology to model and analyze credit card users, and the data is oversampled by SMOTE method. Handling of abnormal values and missing values are performed and the variables are standardized. The range of values is made to fall within the same range. Finally, a logistic regression model is established. The random forest algorithm is used to verify its feasibility and effectiveness.