Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario

Abstract

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.