Analysis of Financing Risk and Innovation Motivation Mechanism of Financial Service Industry Based on Internet of Things

Abstract

It is of practical significance to introduce the Internet of Things technology into the financial service industry and find the driving factors and mechanisms of financial innovation to accelerate the promotion of financial innovation. This article starts from the perspective of banks and other supply chain financial institutions, takes mainstream trading products in the commodity trading market as the research object, uses the LA-VAR model, and fully considers the market price fluctuations and liquidity factors of supply chain financial inventory products. It analyzes the theoretical basis of the continuous innovation of rural financial products. On the basis of analyzing the basic characteristics and types of rural financial product innovation, we explore the connotation of sustainable innovation of rural financial products, clarify the evaluation criteria, and lay a theoretical foundation for continuous dynamic evaluation. Based on technical innovation evaluation theoretical models such as Schumpeter’s innovation model, technical specifications-technological track model, and NR relationship model market, we analyze the innovation elements of rural financial products from the external and internal aspects of innovation and discuss the relationship between the factors. The interaction mechanism of rural financial products has established a dynamic mechanism model for the continuous innovation of rural financial products. A fuzzy comprehensive evaluation was made on the continuous innovation power of financial service industry products in a certain area. Using a combination of remote surveys and on-site visits, a questionnaire survey was conducted on financial service industry institutions in a certain region’s financial system. Each survey object was required to conduct 120 × 1067 index comparisons and use the data after processing the arithmetic average Matlab carries out the objective processing of programming. The results show that the LA-VAR model with liquidity indicators can measure the liquidity risk well and more comprehensively evaluate the risk of the inventory pledge financing model. According to the index weights determined by AHP, the development of the financial service industry will be promoted in a targeted manner from the internal construction of financial institutions and the optimization of the external innovation environment.