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...

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Bibliographic Details
Published in:Discrete dynamics in nature and society 2018-01, Vol.2018 (2018), p.1-9
Main Authors: Liu, Jia, Zhu, Xiaoxia, Li, Shi-Yong
Format: Article
Language:English
Subjects:
Big Data
Cash flow
Credit ratings
Data management
Decision analysis
Decision support systems
Dialysis
Economic models
Finance
Financial institutions
Financial management
Financial risk
Financial services
Financial services industry
Financing
Hydrologic cycle
Hydrologic models
Hydrology
Information systems
Inventory control
Logistics
Management methods
Order quantity
Precipitation
Risk analysis
Risk assessment
Risk aversion
Risk management
Runoff
Small and medium sized companies
Small business
Suppliers
Supply chains
Surface runoff
Theory
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Summary: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.
ISSN:1026-0226
1607-887X
DOI:10.1155/2018/3259858