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.
Highlights
Internet development provides new channels and opportunities for small- and middle-sized enterprises’ (SMEs) financing
Different from the above research work, this paper addresses the key issues in risk management in the process of supply chain financing under big data
The process of hydrological layered dialysis model for forecasting supply chain financial risk based on big data scenario is as follows: we firstly establish a “hydrological database” through collecting data on the trading volume of financing companies, bank cash, inflow and outflow data, and the value of changes in the credit index of smalland medium-sized enterprises
Summary
Internet development provides new channels and opportunities for SME financing. Supply chain finance (SCF) is a recent stream of research aimed at optimizing financial flows through solutions implemented by financial institutions [1]. Basu [6, 7] conducted a research on the prepayment financing model and pointed out that the prepayment of financing orders could effectively solve the lag problems of logistics, and established an online data analysis platform. He believed that online supply chain finance played an important role in shortening the repayment period. Trott [17] proposed the role of supply chain financing in promoting the development of SMEs, pointing out that the risk sources mainly came from finance and operations. We integrate and aggregate the analysis results to determine the risk level of the financing enterprise, which help the financial institutions to make financing decisions
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