@inproceedings{7f418208f23344a5b267a27045e34c0b,
title = "Factors influencing automotive financial loan defaults and risk management strategies",
abstract = "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. {\textcopyright} 2025 held by the owner/author(s).",
keywords = "Auto loans, Car loans, Classification, Regression, Risk management, Supervised learning, XGBoost",
author = "Sibo Peng",
year = "2025",
doi = "10.1145/3729706.3729737",
language = "English",
isbn = "9798400712715",
series = "Proceedings of International Conference on Cyber Security, Artificial Intelligence and the Digital Economy, CSAIDE",
publisher = "Association for Computing Machinery",
pages = "204--209",
booktitle = "Proceedings of 2025 4th International Conference on Cyber Security, Artificial Intelligence and the Digital Economy (CSAIDE 2025)",
address = "United States",
note = "2025 4th International Conference on Cyber Security, Artificial Intelligence and the Digital Economy (CSAIDE 2025), CSAIDE2025 ; Conference date: 07-03-2025 Through 09-03-2025",
}