The Practice Study of Consumer Credit Risk Based on Random Forest
Authors
Cui-zhu Meng, Bi-song Liu, Li Zhou
Corresponding Author
Cui-zhu Meng
Available Online July 2019.
- DOI
- 10.2991/masta-19.2019.17How to use a DOI?
- Keywords
- Consumer credit risk, Loan, Random forest, Auto finance, Data mining
- Abstract
How to evaluate and identify the potential default risk of the borrower before issuing the loan is the basis and important link of the credit risk management of modern financial institutions. Based on the data provided by an auto finance institution, This paper mainly studies how to analyze the historical loan data of auto financial institutions with the help of the idea of unbalanced data classification, and predicts the possibility of loan default based on Random forest classification model, which provides a reference for the risk control of this institution.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Cui-zhu Meng AU - Bi-song Liu AU - Li Zhou PY - 2019/07 DA - 2019/07 TI - The Practice Study of Consumer Credit Risk Based on Random Forest BT - Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019) PB - Atlantis Press SP - 101 EP - 106 SN - 1951-6851 UR - https://doi.org/10.2991/masta-19.2019.17 DO - 10.2991/masta-19.2019.17 ID - Meng2019/07 ER -