Research on Supply Chain Financial Risk Assessment Based on Blockchain and Fuzzy Neural Networks

Abstract

With the development of supply chain finance, the credit risk of small- and medium-sized financing enterprises from the perspective of supply chain finance has arisen. Risk management is one of the key tasks of the credit business of banks and other financial institutions, which runs through all aspects of the credit business before, during, and after the loan. This article combines blockchain and fuzzy neural network algorithms to study the credit risk of SME financing from the perspective of supply chain finance. This article builds a supply chain financial system through blockchain technology and integrates supply chain financial information into blocks. The fuzzy neural network algorithm is used for financial data processing and risk assessment, effectively solving and improving the risk processing level of the supply chain. Through further simulation, the application effect of blockchain and machine learning algorithms in the supply chain financial system was verified.

Analysis of Financial Management and Decision-Making in Institution of Higher Learning Based on Deep Learning Algorithm

“…Hristov et al introduced a deep neural network in DL for early warning of shared financial risks [17]. According to the characteristics and decision-making process of college financial data, Wang studied and analyzed the advantages and disadvantages of the clustering algorithm and classification algorithm, proposed an improved decision tree algorithm based on metrics; and established a college financial management model [18].…”

Research on Supply Chain Finance Driven by Blockchain

“…Prior research has explored the design and working mechanisms of supply chain finance as well as applications of blockchain in supply chain finance [10][11][12][13] . However, few articles have studied from the perspective of mathematical model how blockchain technology affects the decision-making of parties in supply chain finance, making parties choose to perform their contracts.…”

A Type‐3 Fuzzy Approach for Stabilization and Synchronization of Chaotic Systems: Applicable for Financial and Physical Chaotic Systems

“…In [33], an H ∞ -based control system is designed and FLSs are used to investigate the robustness. e risk assessment of financial CSs is investigated in [34], and an FLS-based method is presented. In [35], an FLS-based system is proposed for forecasting applications.…”

“…e literature reviewing show that (i) In most studies, the controller is designed for a special case of CS and the designed controller cannot be applied for a CS with different parameters and dynamics [7-9] (ii) e stability of most reviewed controllers is not guaranteed under perturbations and uncertainties [30,38] (iii) Some type-1 and type-2 neuro-fuzzy controllers have been developed for CSs, but most of them cannot handle the high uncertainties in CS dynamics [33-37] (iv) Most of the previous studies are optimized in an offline scheme [33][34][35][36][37] (v) e robustness against unknown perturbations in CS dynamics needs more studies [9,36,37] Regarding the above discussion, a new type-3 fuzzybased controller is developed for CSs. Most recently, the type-3 FLSs with strong uncertainty modeling capability have been developed.…”