Supply Chain Financial Risk Research Articles

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

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.

Fuzzy hierarchy analytic method of enterprise supply chain financial risk

Supply chain finance refers to one or more companies upstream and downstream of the industrial supply chain. According to the core company of each node, based on actual transactions, use customized and standardized financing transaction methods to control capital flows or control assets. The right to provide comprehensive financial products and services between supply chain nodes. This article only needs to introduce the financial risk analysis of the enterprise supply chain based on the fuzzy analytic hierarchy process. This paper proposes a fuzzy analytic hierarchy process, which uses a combination of qualitative and quantitative risk assessment methods to assess financial risks, and designs a financial risk assessment system by constructing a fuzzy judgment matrix. It also proposes a comprehensive judgment of the financial risk assessment method. The impact of various risk factors on financial services provides a basis for risk prevention. The experimental results of this paper show that the fuzzy analytic hierarchy process evaluation method is relatively objective and can effectively evaluate the financial risks of the enterprise supply chain. From the weight analysis, it can be concluded that the technical risk weight value is 0.47, which accounts for the largest proportion and is the most important risk.

Supply Chain Financial Risk Evaluation of Small- and Medium-Sized Enterprises under Smart City

Prevention and control of risks are an eternal theme of financial institutions. Although, to some extent, the emergence of supply chain finance can enhance the financing capacity of small- and medium-sized enterprises (SMEs) and reduce financial risks of financial institutions, with the development of smart city and smart finance, the financial risks of SMEs are more complex, infectious, dormant, and difficult to accurately identify and measure. Facing this status, financial institutions have been required to understand and evaluate the financial risks of SMEs from a new perspective. Therefore, this paper, based on the study of financial risks assessment of SMEs under the smart city and smart finance, innovatively constructs a new index evaluation system for supply chain finance, based on improved hesitant fuzzy linguistic PROMETHEE method, and the effectiveness and advantages of the model have been verified through an example. To a certain degree, the SMEs financing the evaluation model and improved PROMETHEE method can not only help financial institutions reduce the risks in the specific financial transactions but also reduce the survival threat of financial institutions. Moreover, it is of positive significance to the stable operation of the financial system.

Application of Fuzzy Influence Diagram Evaluation Algorithm in Supply Chain Financial Credit Risk Assessment

Application of Fuzzy Influence Diagram Evaluation Algorithm in Supply Chain Financial Credit Risk Assessment

Application of genetic algorithm and BP neural network in supply chain finance under information sharing

The supply chain finance industry will generate the flow of funds and commodities when providing financing services to small and medium-sized enterprises (SMEs). At this time, banks will face multiple risks such as policy, operation, market and credit. The investigation on supply chain finance under information sharing from the aspect of credit risk assessment will be conducted. The genetic algorithm combined with support vector machine and BP neural network is selected to evaluate the credit risk of supply chain finance. In the support vector machine method, the parameter selection method adopts genetic algorithm. In the included data, the gap in growth capacity of SMEs is relatively large. The standard deviations of main business, net profit and total assets are all above 30%, and the standard deviations of current ratio and quick ratio are small, which means that the two are more stable and healthier. In addition, among all the investigated enterprises, the cost gap is large, and the standard deviation of the inventory decline price reserve is small, which means that most enterprises have good inventory quality. After classification, 32 high-quality enterprises, 46 neutral enterprises and 55 risk enterprises are obtained in the total sample. In the test sample, there are 21 high-quality enterprises, 12 neutral enterprises, and 26 risk enterprises. The overall classification accuracy of the support vector machine method optimized by genetic algorithm is relatively lower than that of the BP neural network method. The classification accuracy of the support vector machine method optimized by genetic algorithm is 76.27%, and the classification accuracy of BP neural network method is 89.83%. The supply chain financial risk assessment of SMEs is mainly explored from the perspective of banks. The results can provide theoretical support for reducing the probability of bank’s profit damage and increasing the bank’s profitability.

Research on Supply Chain Financial Risks and Preventive Measures in the Era of Big Data

The emergence of supply chain finance has reduced the financing costs of SMEs. Due to the development of a diversified supply chain financial subject platform, there is a lack of risk control in terms of theory and practice. Big data is generated in Internet applications and combined with information technology to form big data technology. It can provide financial institutions with large-scale data analysis methods and can effectively improve the efficiency and ability of financial institutions to serve supply chain members. However, big data has some problems, such as higher processing cost, lower authenticity, and difficulty in effectively protecting the privacy and security of users. There are many problems with this new development model. This article focuses on the risk problems faced by supply chain finance. It discusses the use of big data technology to effectively solve the supply chain financial risk problems, and gives some measures that can be effectively solved for how to effectively avoid these risk factors. By effectively solving the financial risk problem in the era of big data, it provides guarantee for the benign development of enterprises, and provides a certain reference for researchers engaged in related fields and workers in this field.

Regression analysis of supply chain financial risk based on machine learning and fuzzy decision model

Supply chain financial risk control has become the biggest obstacle restricting financial institutions from developing supply chain financial services. If financial institutions want to make a difference in the supply chain finance business, they need to implement strong control measures against supply chain finance risks. Based on the research of supply chain finance and risk related theories, this paper uses fuzzy preference relations and selects the main risk criteria to construct a risk evaluation index system. Moreover, this paper takes the development of J company’s supply chain finance business as a background to conduct an empirical analysis and proposes the risk management measures and development strategies of J company’s actual supply chain finance business. The combination of quantitative analysis and qualitative evaluation is more comprehensive and operational value for the research and management of supply chain financial risks. In addition, this paper uses the evaluation index system constructed in this paper to conduct a more comprehensive summary and analysis of the internal and external environment of risk influencing factors. The research results show that the performance of the model constructed in this paper is good.

Measuring Risks of Confirming Warehouse Financing from the Third Party Logistics Perspective

Confirmation warehouse financing is an important model in supply chain finance. This type of financing has special characteristics due to the existence of the reverse repurchase link, and it increases the risk commitment of the core enterprise at a certain level. Previous research on supply chain financial risk mostly settled in ‘all-industry, multi-model’, ignoring the special risks of single mode. To supplement the vacancies in the current research, the special risks of supply chain finance should be identified under a single model. On this basis, a measurement index system for confirmation warehouse financing risk is created. The article uses a Back Propagation (BP) neural network to build a Third Party Logistics (3PL) perspective of the risk measurement model for confirmation warehouse financing. The said network is combined with the 24 sets of actual cases from ZY Logistics. MATLAB is used to train the sample data. Results show that the absolute errors—0.042998, −0.011102, 0.020514 and 0.039448—between the training value and the predicted value are smaller than the preset error value. Among the 24 cases, high-risk businesses reached 41.7%, whereas low-risk businesses only accounted for 29.2%. The ZY enterprise confirms that warehouse financial business risk is high, and this situation should be revised. Research shows that the risk measurement indicator system has good risk prediction ability. This study establishes and verifies the rationality of the risk measurement index system and provides a reliable reference for 3PL risk aversion in supply chain finance.

System dynamics analysis of supply chain financial management during capacity expansion

Purpose This study aims to describe in detail, a system dynamics-based study that was used to show how a large vertically integrated aquaculture company should approach its stepwise capacity expansion program, without undermining its financial performance or affecting the performance of the value chain. Design/methodology/approach The company and its aquaculture value chain are used as case study. A system dynamics model is developed on the basis of generic end-to-end agribusiness and aquaculture supply chain models. The model includes the unique dynamics relating to an aquaculture supply chain. Also modelled is the working capital management rules of the company, with the effects of the capacity expansion program on its working capital, market share and its supply chain obligations. The model is used to determine the long-term impact of the company’s working capital management under different modes of financing and rate of expanding the capacity. Findings For a large vertically integrated company that wants to increase its capacity, there is a systematic approach to working capital management that can be used to avoid financial distress or value chain distortion. Research limitations/implications Extended the scope of system dynamics modelling within multiple disciplines, namely, agribusiness supply chain finance, supply chain capacity investment, financial management in large companies, supply chain working capital management and aquaculture value chain. Practical implications The developed model can be used to manage supply chain working capital in large vertically integrated agribusinesses, and also to assess supply chain financial risk. Originality/value To enhance the model build, discrete event simulation was used to model aspects of the system. The eventual system dynamics-discrete event simulation model is a form of hybrid simulation modelling that was used to provide a deeper understanding of how supply chain financial decisions affect an entire value chain system.

Big data analytics on enterprise credit risk evaluation of e-Business platform

In recent years, the research on supply chain finance has been mature, in the supply chain financial risk research, the research on credit risk is mostly. However, there is little research on online supply chain finance, especially on credit risk. Therefore, this article has carried on the detailed research to the commercial bank online supply chain financial credit risk assessment. Firstly, the article applies the literature induction method to review the supply chain financial credit risk indicators, add the “online” specific indicators to supplement, combine the indicators selection principle to determine the final indicators, and construct the commercial bank online supply chain financial credit risk assessment index system, select online The supply chain financial business carried out the concentrated SMEs in the automobile manufacturing industry as the research object, using the nonlinear LS-SVM model for empirical analysis, and compared with the logistic regression model results. Secondly, the designed index system can effectively evaluate credit risk. The classification accuracy of LS-SVM evaluation model is higher than that of Logistic regression model and it has strong generalization ability. It can comprehensively identify the credit risk of small and medium-sized financing enterprises, and provide a reasonable and scientific analysis and support tool for assessing SME credit risk. Finally, combined with the fierce competition background of supply chain finance, it is proposed that commercial banks should actively carry out online supply chain finance, comprehensive risk management and deepen cooperation with e-Business platforms and logistics platforms.

Research on financial risk management model of internet supply chain based on data science

The supply chain financial risk management model is established under the Internet financial model, so as to improve the supply chain's ability and resist risks, a financial risk management model of Internet supply chain is proposed based on data science. Firstly, the forms and countermeasures of financial risk in Internet supply chain is analyzed. The data science analysis method is used to analyze the risk management model, and the supply chain financial risk management model is constructed based on the multivariate piecewise regression analysis and the financing decision effectiveness game. The relationship between supply chain financial risk management and enterprise performance is studied under the Internet finance model, the validity of the financing decision of Internet supply chain is analyzed, and it constructs the decision model of Internet supply chain by descriptive statistical analysis method. The fuzzy decision method is used to analyze the risk assessment of Internet supply chain, and the Simunic model is used to analyze the correlation model of risk management in Internet supply chain. In order to improve the risk management ability of Internet supply chain, the statistical analysis and comprehensive decision of risk control are carried out by using piecewise sample regression analysis. The results of empirical data analysis show that the model has good piecewise fit for financial risk management and management of supply chain under Internet financial model, and the accuracy of data evaluation is high. The model is robust for financial risk management and performance evaluation of supply chain.

Construction of Supply Chain Financial Risk Management Mode Based on Internet of Things

The objective of this paper is to analyze the problems of information asymmetry, mode lag, and high operational risk between banks and enterprises in the practice of supply chain finance business. In this paper, first, the concept and function of supply chain finance were elaborated, and the theoretical basis of supply chain financial risk control was studied. The implement of principal agent, information asymmetry theory, and self-compensated trade finance theory on realizing the risk avoidance in the actual development process was analyzed. Then, the business processes of the three basic financing models of supply chain finance and the shortcomings in the operation of each model were proposed. On this basis, risk sources and influencing factors of supply chain finance were analyzed, and operational risks were emphasized. Finally, a new financing mode of inventory pledge was designed based on the special function of Internet of Things (IoT) technology and the business process of inventory financing mode. The results showed that there was a big gap in the value of risk loss caused by various types of loss events in the operation risk of supply chain finance inventory pledge financing mode; among them, the biggest losses were from external fraud. By comparing the expected loss (ES) value of inventory pledge financing mode based on the IoT with the operation risk of traditional mode, it could be found that the supply chain finance mode based on the IoT technology effectively reduced the operation risk. Therefore, the system designed in this paper has certain reference and practical significance for financial risk management.

Quantifying Credit Risk of Supply Chain Finance: A Chinese Automobile Supply Chain Perspective

Credit risk is a major risk of supply chain finance business, and it has recently gained increasing attention. Due to the high dependence between enterprises, the assessment of the supply chain finance risk will be more complicated. In the current study, the research subjects are almost the single financing enterprises, and the credit risk of financing enterprise portfolio in supply chain finance has rarely been discussed. This study aims to establish a complete risk assessment model, which is based on modified KMV model and Copula function, to quantify the credit risk of enterprises in supply chain finance. Based on the model, we can measure the credit risk of the single financing enterprise and financing enterprise portfolio (up- and down-stream) in supply chain. The results indicate that default contagion does exist in supply chain and the intensity of default contagion between enterprises in the financing enterprise portfolio is asymmetrical. Moreover, the conclusions about the joint expected default frequency and conditional expected default frequency of financing enterprise portfolio in supply chain, are of great significance to commercial banks and other financial institutions.

Risk Evaluation and Control of Supply Chain Finance

As an effective way of enterprises financing, supply chain finance has attracted much attention in recent years. However, since supply chain finance has some problems like long financing period, numerous stakeholders and complex effects, banks are at a higher risk carrying out this kind of service. The purpose of this paper is to explore the key factors in supply chain finance risk assessment and study the effective mode of risk elevation. Based on the existing literature and research, this paper uses Z-score to standardize the financial index of 344 medium-sized enterprises in automotive industry chain from October, 2016 to October, 2017 and build a model of supply chain risk assessment and control basing on analytic hierarchy process, principal components analysis and logistic regression analysis. Finally, we summarize how each index affects risk assessment and then analyze the reasons.

Financial Credit Risk Evaluation Based on Core Enterprise Supply Chains

Supply chain finance has broken through traditional credit modes and advanced rapidly as a creative financial business discipline. Core enterprises have played a critical role in the credit enhancement of supply chain finance. Through the analysis of core enterprise credit risks in supply chain finance, by means of a ‘fuzzy analytical hierarchy process’ (FAHP), the paper constructs a supply chain financial credit risk evaluation system, making quantitative measurements and evaluation of core enterprise credit risk. This enables enterprises to take measures to control credit risk, thereby promoting the healthy development of supply chain finance. The examination of core enterprise supply chains suggests that a unified information file should be collected based on the core enterprise, including the operating conditions, asset status, industry status, credit record, effective information to the database, collecting related data upstream and downstream of the archives around the core enterprise, developing a data information system, electronic data information, and updating the database accurately using the latest information that might be available. Moreover, supply chain finance and modern information technology should be integrated to establish the sharing of information resources and realize the exchange of information flows, capital flows, and logistics between banks. This should reduce a variety of risks and improve the efficiency and effectiveness of supply chain finance.

Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario

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.

Internet Supply Chain Financial Model and Risk Management Research Based on Blockchain Technology

Internet subversion and innovation make the supply chain finance moving in the direction of the Internet, that is, the Internet supply chain finance. This article considers the main body of the internet supply chain which began to become diversified, that individuals also participate in the Internet supply chain finance, from financial institutions, core enterprises to the upstream and downstream SMEs, and logistics companies. The study focuses on the Internet supply chain financial model supported by big data and the related risk factors. To solve the risk factors it demonstrates the application mechanism and framework of the blockchain technology in the financial risk management of Internet supply chain. It also take the receivable account of Internet supply chain as an example to explain how to use the blockchain technology in Internet supply chain finance.

Supply chain finance risk management

Based on mechanism design theory on asymmetric information, this study devises a selectable menu of contracts with price discounts and trade credit rates and proposes strategies for mitigating the risk of payment default when a tourism service provider trades with unfamiliar tour operators. Tourism service providers are suggested to remain conservative under a positive economic condition and progressive under a moderate economic condition when engaging in a new business opportunity. However, a fringe level of progressivism may be maintained under a moderate economy and a low-risk environment. The net benefit of tourism service providers increases when the bank rate is high, and the profit of the entire tourism channel increases when the percentage or ability to pay reliable agents is high.

A Internet Supply Chain Operational Risk Integrated Model Based on Copula-POT

This paper will use risk management theory, select risk identification and risk measurement methods, to deeply analyse the Internet supply chain financial operational risk. Based on the classification of operational risk using FTA method, this paper selects the POT model which is used to measure the operational risk of the Internet supply chain finance. Considering the correlation between different loss types of operational risk, Copula function is introduced to better integrate the measurement Internet Supply Chain Financial Operational Risk. Finally, through empirical research, we will analyse the results.

Generalized Pythagorean Fuzzy Maclaurin Symmetric Means and Its Application to Multiple Attribute SIR Group Decision Model

Pythagorean fuzzy sets, originally proposed by Yager (IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), pp 57–61, 2013), which are convenient to describe vagueness and impression in real-world problems, can be regarded as a novel extension of intuitionistic fuzzy sets. Generalized Maclaurin symmetric mean comes as an extension of classical Maclaurin symmetric mean. Their prominent feature is that they can both capture overall interrelationships and also focus on individual importance among the arguments. However, the generalized Maclaurin symmetric mean can only be considered for aggregating numeric information. In this paper, we investigate the generalized Maclaurin symmetric mean and further extend to Pythagorean fuzzy environment. We develop the Pythagorean fuzzy generalized Maclaurin symmetric mean (PFGMSM), the Pythagorean fuzzy weighted generalized Maclaurin symmetric mean (PFWGMSM), and discuss a variety of their desirable properties. To better understand the proposed parameterization operators, we provide an innovative way to extend the classical superiority and inferiority ranking (SIR) group decision model to Pythagorean fuzzy environment. Then, an approach formed with the aid of PFWGMSM and SIR to multiple attribute group decision-making with Pythagorean fuzzy information is developed. Finally, an illustrative example concerning supply chain financial risk decision-making is provided to verify the proposed method and demonstrate its effectiveness.