Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario
In recent years, internet development provides new channels and opportunities for small- and middle-sized enterprises’ (SMEs) financing. Supply chain finance is a hot topic in theoretical and practical circles. Financial institutions transform materialized capital flows into online data under big da...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2018/3259858 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832549198567833600 |
|---|---|
| author | Jia Liu Shiyong Li Xiaoxia Zhu |
| author_facet | Jia Liu Shiyong Li Xiaoxia Zhu |
| author_sort | Jia Liu |
| collection | DOAJ |
| description | In recent years, internet development provides new channels and opportunities for small- and middle-sized enterprises’ (SMEs) financing. Supply chain finance is a hot topic in theoretical and practical circles. Financial institutions transform materialized capital flows into online data under big data scenario, which provides networked, precise, and computerized financial services for SMEs in the supply chain. By drawing on the risk management theory in economics and the distributed hydrological model in hydrology, this paper presents a supply chain financial risk prediction method under big data. First, we build a “hydrological database” used for the risk analysis of supply chain financing under big data. Second, we construct the risk identification models of “water circle model,” “surface runoff model,” and “underground runoff model” and carry on the risk prediction from the overall level (water circle). Finally, we launch the supply chain financial risk analysis from breadth level (surface runoff) and depth level (underground runoff); moreover, we integrate the analysis results and make financial decisions. The results can enrich the research on risk management of supply chain finance and provide feasible and effective risk prediction methods and suggestions for financial institutions. |
| format | Article |
| id | doaj-art-d6d3305dfc4c418ba157c2d11c12f045 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-d6d3305dfc4c418ba157c2d11c12f0452025-02-03T06:12:01ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2018-01-01201810.1155/2018/32598583259858Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data ScenarioJia Liu0Shiyong Li1Xiaoxia Zhu2School of Economics and Management, Yanshan University, Qinhuangdao 066004, ChinaSchool of Economics and Management, Yanshan University, Qinhuangdao 066004, ChinaSchool of Economics and Management, Yanshan University, Qinhuangdao 066004, ChinaIn recent years, internet development provides new channels and opportunities for small- and middle-sized enterprises’ (SMEs) financing. Supply chain finance is a hot topic in theoretical and practical circles. Financial institutions transform materialized capital flows into online data under big data scenario, which provides networked, precise, and computerized financial services for SMEs in the supply chain. By drawing on the risk management theory in economics and the distributed hydrological model in hydrology, this paper presents a supply chain financial risk prediction method under big data. First, we build a “hydrological database” used for the risk analysis of supply chain financing under big data. Second, we construct the risk identification models of “water circle model,” “surface runoff model,” and “underground runoff model” and carry on the risk prediction from the overall level (water circle). Finally, we launch the supply chain financial risk analysis from breadth level (surface runoff) and depth level (underground runoff); moreover, we integrate the analysis results and make financial decisions. The results can enrich the research on risk management of supply chain finance and provide feasible and effective risk prediction methods and suggestions for financial institutions.http://dx.doi.org/10.1155/2018/3259858 |
| spellingShingle | Jia Liu Shiyong Li Xiaoxia Zhu Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario Discrete Dynamics in Nature and Society |
| title | Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario |
| title_full | Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario |
| title_fullStr | Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario |
| title_full_unstemmed | Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario |
| title_short | Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario |
| title_sort | hydrological layered dialysis research on supply chain financial risk prediction under big data scenario |
| url | http://dx.doi.org/10.1155/2018/3259858 |
| work_keys_str_mv | AT jialiu hydrologicallayereddialysisresearchonsupplychainfinancialriskpredictionunderbigdatascenario AT shiyongli hydrologicallayereddialysisresearchonsupplychainfinancialriskpredictionunderbigdatascenario AT xiaoxiazhu hydrologicallayereddialysisresearchonsupplychainfinancialriskpredictionunderbigdatascenario |