Proceedings of the Sixth Symposium of Risk Analysis and Risk Management in Western China (WRARM 2019)

Research on Risk Assessment of Supply Chain Financing for Big Data Enterprises

Authors
Jian He, Hongmei Zhang
Corresponding Author
Jian He
Available Online September 2019.
DOI
https://doi.org/10.2991/wrarm-19.2019.7How to use a DOI?
Keywords
Supply Chain Finance, Big Data Enterprises, Financing risk
Abstract

Under the background of current enterprise financing difficulties, the development of supply chain finance has become one of the important options for enterprise financing. Based on principal component analysis and logistic regression model analysis, this paper chooses evaluation indexes from profitability and growth ability, and establishes a supply chain default risk early warning model for large data enterprises. Empirical results show that its early warning accuracy is high, which provides a new idea for the early warning research of supply chain financing risk for large data enterprises.

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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Volume Title
Proceedings of the Sixth Symposium of Risk Analysis and Risk Management in Western China (WRARM 2019)
Series
Advances in Intelligent Systems Research
Publication Date
September 2019
ISBN
978-94-6252-793-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/wrarm-19.2019.7How 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  - Jian He
AU  - Hongmei Zhang
PY  - 2019/09
DA  - 2019/09
TI  - Research on Risk Assessment of Supply Chain Financing for Big Data Enterprises
BT  - Proceedings of the Sixth Symposium of Risk Analysis and Risk Management in Western China (WRARM 2019)
PB  - Atlantis Press
SP  - 33
EP  - 38
SN  - 1951-6851
UR  - https://doi.org/10.2991/wrarm-19.2019.7
DO  - https://doi.org/10.2991/wrarm-19.2019.7
ID  - He2019/09
ER  -