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.
At present, the development of the Internet of Things in the financial services industry is in its infancy in the world and has a certain technology, industry, and application foundation, showing a good development trend. The mature Internet of Things technology can realize the connection of all items to the network, thus facilitating people’s identification and management control of items [
As the Internet of Things technology continues to mature, it is being promoted and used at home and abroad, and the financial industry is no exception [ First, we perform a comparison with the value of risk when single-variety supply chain financial inventory is used as pledge and explain the empirical results reasonably. The calculation results of the model show that the continuous innovation power of financial service industry products in a certain region is average. Second, the study confirmed that, as one of the six central provinces and three northeastern provinces to carry out innovation pilot projects, under the leadership of the provincial party committee and the provincial government, various financial institutions have intensified innovation in the financial service industry and achieved certain results. However, compared with the eastern coastal areas, the overall level of the financial industry in a certain area is still relatively backward, the development of the financial service industry is obviously lagging behind, and the continuous innovation of financial service products is a general reality.
Under the premise of ensuring the rationality and stability of the research data, this paper uses the LA-VAR model to quantify the liquidity risk and market risk of the supply chain financial inventory portfolio composed of rebar and palm oil. Specifically, the technical contributions of this article can be summarized as follows:
The rest of this article is organized as follows. Section
In the entire process of the supply chain, cash flow gaps have been generated in prepayments, supply chain financial inventories, accounts receivable, and other links. By classifying these links, three business models of supply chain finance have emerged: confirmation warehouse financing, warehouse financing, and accounts receivable financing.
For core enterprises, confirming warehouse financing firstly enhances dealers’ sales capabilities, solves the problem of product backlog, expands the market share of products, and obtains greater commercial profits. Secondly, it locks in sales channels in the fierce market competition and obtains the competitive advantage of the industrial chain. Thirdly, there is no need to finance from banks, which not only reduces the cost of funds, but also reduces the occupation of accounts receivable and guarantees collection. For small- and medium-sized enterprises, there is no need to worry about the difficulty of purchasing goods due to shortage of funds. The bank provides financing facilities for them and obtains preferential prices from upstream core enterprise suppliers. Products with large gender differences can also be ordered in off-season and sold in peak seasons to obtain higher profits. For banks, through the business of confirming warehouses, they not only obtain abundant service fees and possible bill discount fees but also master the power to withdraw goods.
The business process is shown in Figure
Schematic diagram of financial services industry financing and warehouse model.
Specifically, accounts receivable financing refers to the conditional transfer of accounts receivable formed by credit sales by the demander of funds to a special financing institution, which will provide enterprises with financing, debt recovery, and sales account management, so that companies that use credit sales as the main method can obtain the necessary funds and strengthen the turnover of funds. Generally, accounts receivable financing uses accounts receivable vouchers as the subject matter pledge or transfer, and the term does not exceed the age of the accounts receivable. The main entities are small- and medium-sized enterprises (debt enterprises), core enterprises (debt enterprises), and commercial banks. According to the different sources of repayment, the financing model of accounts receivable is divided into pledge of accounts receivable and transfer of accounts receivable. The first source of repayment for pledge of accounts receivable is the sales income of creditor companies, and the second source of repayment is the accounts receivable paid by the debtor company; and the first source of repayment for the transfer of accounts receivable is directly from the debtor company. For the accounts receivable paid to the bank, the second source of repayment is the sales revenue of the creditor enterprise. In addition, the financing of accounts receivable can also be introduced as a third-party guarantee by logistics companies, as shown in Figure
Schematic diagram of accounts receivable financing model under the Internet of Things environment.
Through accounts receivable financing, small- and medium-sized enterprises not only obtain funds without increasing their own liabilities but also improve the company’s asset-liability structure. When the sales volume increases, the company can directly convert a large number of sales invoices into funds, with higher flexibility; and the cost is relatively low. At the same time, accounts receivable also have the characteristics of short financing time and high efficiency. The active financing of accounts receivable can also strengthen internal management and make decision-making more scientific.
As a financial innovation, the risks faced by supply chain finance include credit risk, financial risk, operational risk, warehouse receipt pledge risk, and information transmission risk. Therefore, how to effectively identify and prevent these risks is the key to the success of supply chain finance.
Under the traditional credit model, the bank pays attention to the static financial data of a single enterprise, which is the possibility of default caused by the enterprise itself. But, in the context of supply chain, risk is affected not only by the enterprise’s own risk factors but also by the supply chain [
Traditional credit risk is the risk faced by a single enterprise itself; supply chain financial risk is distributed across the entire supply chain centered on the core enterprise [
In view of the stability of the model, this paper chooses principal component analysis as the scoring method. Since the model has no assumptions about the distribution of variables, it is not required to assume that the indicators have a multivariate normal distribution. The specific evaluation principles are as follows: Select a sample to score the indicators and scoring standards in the above list, and each indicator is denoted by Standardize the initial data; the standardized formula is The standardized matrix is obtained after standardization: Calculate the correlation coefficient between every two indicators in the standardized matrix, and obtain the correlation coefficient matrix Select the first
The Internet of Things provides the conditions for “visual tracking.” Manufacturing companies mainly apply the Internet of Things to the “supply chain management” of the enterprise. Financial products, innovative products in this area, are called “supply chain finance.” From the perspective of financial institutions providing services, supply chain finance refers to financial institutions and other third-party service institutions that comprehensively grant credit to the entire supply chain of the industry.
The visual tracking provided by the Internet of Things for financial service industry innovation can solve the above three problems. The process of optimizing supply chain financing is shown in Figure
Financial commercial bank service process.
As shown in Figure
Trends of rebar median price and palm oil median price.
Because the data for CARCH model fitting must be a stationary series, as otherwise it will cause false regression, making the experimental results meaningless, it is necessary to test the stationarity of the time series of future prices to ensure that there is no autocorrelation relationship between the data and time, and it is a stationary series. At present, the most commonly used method of sequence stationarity test is the ADF unit root test, which performs unit root test on the time series of price changes of rebar and palm oil.
The unit root test result of the price change series shows that the
After determining that the price change sequences of the two collaterals are stationary, the next step is to start the fitting estimation of the GARCH family model. First, you check whether the price change sequence of rebar and palm oil follows a normal distribution, as shown in Figure
Normal distribution test of rebar and palm oil price change series.
The average value of the price change series of palm oil is −0.000144, the standard deviation is 0.009328, the skewness is 0.450833, and the skewness is greater than 0, indicating that the price series distribution of palm oil has a long right tail phenomenon.
The GARCH model is constructed for the price change sequence of the two collaterals. The GARCH family models mainly include the GARCH model, the T-GARCH model, and the E-GARCH model. This paper studies the construction of low-level GARCH (1, 1), T-GARCH (1, 1), and E-GARCH (1, 1) models. The GARCH fitting parameter diagram of the rebar price change sequence and the GARCH fitting parameter diagram of the palm oil price change sequence are shown in Figures
GARCH fitting parameter diagram of rebar price change sequence.
GARCH fitting parameter diagram of palm oil price change series.
It can be seen from Figure
ARCH LM test results of the volatility fitting residuals of the rebar and palm oil price change series.
It can be seen from Figure
Normal distribution test of supply chain financial inventory portfolio price change rate series.
Three GARCH models are fitted to the sequence of supply chain financial inventory portfolio price changes, and the results are shown in Figure
GARCH model fitting parameter diagram of supply chain financial inventory portfolio price change sequence.
It can be seen from Figure
The result of the hypothetical parameter results of the sequence distribution of supply chain financial inventory portfolio price changes.
Under a certain sampling method, the larger the sample size, the higher the estimation accuracy, but considering the actual situation, it is impossible to conduct a study without capacity.
Because the survey uses the existing PBC mail network, it is basically not limited by fees; the survey is conducted between subordinates, managing departments, and managed departments, and questionnaires such as “no answer” and “invalid” basically do not exist. It is carried out in high-quality population, the overall sample is very similar, extreme differences basically do not exist, and the degree of sample heterogeneity can be ignored.
The questionnaire was sent to the central branch of the People’s Bank of China through the Lotus system of the People’s Bank of China. Each branch was required to complete 77 questionnaires. A regional branch office itself also completed 16 questionnaires, and the corresponding financial service institutions completed 15 questionnaires. The CVR inspection data is shown in Figure
Proportion of CVR inspection data.
The content validity of each index is tested based on the test data of CVR, and the test results are shown in Figure
CVR test value.
Analytic hierarchy process (AHP) is a method to deal with economic, management, and technical issues with complex factors. It decomposes a complex problem into multiple components and further decomposes these factors according to the dominance relationship and arranges them according to the target level, the criterion level, and the index level to form a multiobjective, multilevel model.
The analytic hierarchy process is essentially a way of decision-making thinking. It decomposes complex decision-making problems into different components according to the nature of the problem and the general goal required to achieve and then integrates the judgment of people to determine the overall order of the relative importance of the factors in decision-making. The analytic hierarchy process embodies the basic way of decision-making thinking, namely, decomposition, judgment, and synthesis. The basic idea is to transform the overall judgment of the weights of multiple factors that make up a complex problem into a “pairwise comparison” of these factors and then to judge the overall weight of these elements and finally establish the weight of each factor. RI value is shown in Figure
Average consistency index.
Using the basic principles of AHP, the data obtained by 120 × 1067 comparisons of indicators according to Satty’s scale are written in matrix form, and the eigenvalues and eigenvectors are calculated by Matlab, and the eigenvectors corresponding to the
The risk value of a supply chain financial inventory combination using rebar and palm oil under all set pledge periods or confidence levels is less than the corresponding risk value of a single variety of supply chain financial inventory pledge of rebar or palm oil. This shows that in the supply chain financial inventory pledge financing business, the supply chain financial inventory portfolio composed of supply chain financial inventory products that are negatively correlated with market price fluctuations is used as the pledge, which can effectively lower banks and other supply chain financial institutions. The calculation results of the pledge rate model established based on LA-VAR show that when the supply chain financial inventory combination constructed by rebar and palm oil is used as pledge, the pledge rate is inversely proportional to the pledge period. The longer the pledge time, the lower pledge rate of the inventory portfolio, but the higher the pledge rate when the corresponding single species is used as pledge under any circumstances. This means that small- and medium-sized enterprises can use a combination of supply chain financial inventory pledge financing strategies with a negative relationship, compared to only using a single-variety supply chain financial inventory pledge, and can obtain more funds under the same overall value of the pledge. The risks borne by banks and other financial institutions have not increased. According to the basic principles of fuzzy mathematics, the evaluation index is determined according to KPI⟶CVR which is used for index content validity test⟶AHP determining index weight. The 1067 pieces of data obtained from the province’s survey and the 120 × 1067 evaluation index pairwise comparison data are substituted into the model.
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
The authors declare that there are no conflicts of interest.
All authors have approved the publication of the manuscript.