Compressed Sensing and its Applications in Risk Assessment for Internet Supply Chain Finance Under Big Data

Abstract

Plenty of research focuses on supply chain finance and its risk, qualitatively or quantitatively. However, there are only a little literature studies on the Internet supply chain finance (ISCF), especially on its risk by quantitative analysis. After analyzing the information of partners’ panorama data and upstream and downstream data in the internet supply chain, this paper constructs a multiple dimensional intelligent risk assessment system for ISCF. By using the analytic hierarchy process gray assessment theory, a risk assessment model of ISCF is built. Based on the collected big data, tracking and monitoring partners’ panoramic data, and upstream and downstream data of the supply chain in real time, the risk of ISCF can be calculated through the assessment model, so the investor can decide whether to finance or not before lending and monitor the lender dynamically after loaning. Taking Zhong-ken Supply Chain Co., Ltd., a focal company in the supply chain, as an example, this paper evaluates the risk of lending to one financing enterprise and obtains a specific risk value, by which to describe the risk degree. Therefore, the model has certain practicability.