Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)

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/).

Download article (PDF)

Volume Title
Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
Series
Advances in Intelligent Systems Research
Publication Date
July 2019
ISBN
978-94-6252-761-4
ISSN
1951-6851
DOI
10.2991/masta-19.2019.17How to use a DOI?
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  -