Proceedings of the 3rd International Seminar on Education Innovation and Economic Management (SEIEM 2018)

Review of Domestic Application Research of Big Data Mining Technology-SVM in Credit Risk Evaluation

Authors
Mu Zhang, Lu-jing Pang
Corresponding Author
Mu Zhang
Available Online January 2019.
DOI
https://doi.org/10.2991/seiem-18.2019.64How to use a DOI?
Keywords
big data mining technology; support vector machine; credit risk; credit risk Evaluation; Journals reviewed
Abstract
As a classification model in large data mining technology, support vector machine (SVM) has been developing and improving continuously, it has been applied to the field of credit risk more and more widely. The effective evaluation of credit risk by support vector machine is beneficial to the development of banks and enterprises. This paper mainly combs the domestic literature from three aspects: data preprocessing, application and improvement, and integrated combination discrimination of support vector machine in credit risk assessment. Finally, a brief review based on the domestic literature is made. Through the collation of journals reviewed, we can better understand the specific application status of support vector machine in the field of credit risk and lay the foundation for the follow-up research work.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Seminar on Education Innovation and Economic Management (SEIEM 2018)
Part of series
Advances in Social Science, Education and Humanities Research
Publication Date
January 2019
ISBN
978-94-6252-649-5
ISSN
2352-5398
DOI
https://doi.org/10.2991/seiem-18.2019.64How 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  - Mu Zhang
AU  - Lu-jing Pang
PY  - 2019/01
DA  - 2019/01
TI  - Review of Domestic Application Research of Big Data Mining Technology-SVM in Credit Risk Evaluation
BT  - 3rd International Seminar on Education Innovation and Economic Management (SEIEM 2018)
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/seiem-18.2019.64
DO  - https://doi.org/10.2991/seiem-18.2019.64
ID  - Zhang2019/01
ER  -