Proceedings of the 1st International Conference on Business, Economics, Management Science (BEMS 2019)

P2P Default Risk Prediction based on XGBoost, SVM and RF Fusion Model

Authors
Guanlin Li, Yuliang Shi, Zihao Zhang
Corresponding Author
Guanlin Li
Available Online May 2019.
DOI
https://doi.org/10.2991/bems-19.2019.83How to use a DOI?
Keywords
risk prediction, XGBoost, SVM, RF, fusion model.
Abstract
In the P2P platform, the problem of overdue repayment of users often occurs. This phenomenon seriously damages the interests of the platform and creditors. Therefore, how to improve and improve the risk monitoring capability of the P2P online lending platform and reduce the investment risk of investors is the future development of the P2P online lending industry. Very important question. To solve this problem, this paper proposes a P2P default risk prediction model based on XGBoost, SVM and RF fusion model. The model uses the stacking model set framework to model XGBoost, SVM and RF, and combines the advantages of high accuracy, robustness and generalization ability of the three models. The proposed fusion model has better prediction. Effect.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
1st International Conference on Business, Economics, Management Science (BEMS 2019)
Part of series
Advances in Economics, Business and Management Research
Publication Date
May 2019
ISBN
978-94-6252-720-1
ISSN
2352-5428
DOI
https://doi.org/10.2991/bems-19.2019.83How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Guanlin Li
AU  - Yuliang Shi
AU  - Zihao Zhang
PY  - 2019/05
DA  - 2019/05
TI  - P2P Default Risk Prediction based on XGBoost, SVM and RF Fusion Model
BT  - 1st International Conference on Business, Economics, Management Science (BEMS 2019)
PB  - Atlantis Press
SN  - 2352-5428
UR  - https://doi.org/10.2991/bems-19.2019.83
DO  - https://doi.org/10.2991/bems-19.2019.83
ID  - Li2019/05
ER  -