Personal Credit Evaluation Under the Big Data and Internet Background Based on Group Character

DOI

10.2991/msbda-19.2019.49How to use a DOI?

Keywords

SVM, Logistic, Personal credit, Combination model

Abstract

Personal credit evaluation is one of the important means of financial risk prediction.Ttraditional method of personal credit evaluation is Single model analysis. In order to accurately evaluate personal credit and reduce the default loss caused by credit economy to internet finance, combinatorial thinking is needed. In this paper, SVM model and Logistic regression model are analyzed by single analysis, and we set up SVM-Logistic combination model. The results show that the SVM-Logistic model has higher robustness andaccuracy.

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/).

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TY  - CONF
AU  - Cheng Liu
AU  - Dan Wang
AU  - Wenxin Wang
AU  - Zhenyi Ji
PY  - 2019/08
DA  - 2019/08
TI  - Personal Credit Evaluation Under the Big Data and Internet Background Based on Group Character
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)
PB  - Atlantis Press
SP  - 318
EP  - 323
SN  - 2352-538X
UR  - https://doi.org/10.2991/msbda-19.2019.49
DO  - 10.2991/msbda-19.2019.49
ID  - Liu2019/08
ER  -