Factors influencing automotive financial loan defaults and risk management strategies

The purpose of this paper is to identify the key factors that influence customers to default on their car loans and to develop a robust model to predict whether a customer will default. We use linear regression to determine the statistical significance of variables, analyze important variables that affect default, and compare the fitting and prediction results of four classification models (Logistic regression, Decision tree, Random tree, and XGBoost). Finally, this paper points out important variables for financial institutions and provides good suggestions and demonstrations for banks to build models.