scholarly journals Research On The Identification And Evaluation Of Supply Chain Finance Credit Risk

2021 ◽  
Vol 275 ◽  
pp. 01069
Author(s):  
Yinuo Liu
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Financial Institutions ◽  
Credit Rating ◽  
Regulatory System ◽  
Good News ◽  
Credit Risks ◽  
Supply Chain Finance ◽  
Effective Manner ◽  
Financial Credit

Narrow financing channels and high costs have gradually become the main factors restricting the development of small and medium-sized enterprises. The break of the capital chain will restrict the development of the overall supply chain. The emergence of supply chain finance has brought good news for solving the problem of limited capital of small and medium-sized enterprises. However, affected by many factors such as the imperfection of the existing regulatory system, the inefficiency of the credit rating work of financial institutions, and the low moral standards of supply chain member companies, financial institutions bear huge credit risks. Based on the current research of supply chain financial credit risk, this paper analyzes the causes and characteristics of supply chain financial credit risk, finds out the problems and their causes in the identification and evaluation of supply chain financial credit risk, and how to identify and effectively identify and analyze the supply chain financial credit risk in a more timely and effective manner. It is expected to provide reference for financial institutions to strengthen the financial credit risk management of supply chain.

scholarly journals Financial Credit Risk Evaluation Based on Core Enterprise Supply Chains

2018 ◽  
Vol 10 (10) ◽  
pp. 3699 ◽  
Author(s):  
WeiMing Mou ◽  
Wing-Keung Wong ◽  
Michael McAleer
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Supply Chains ◽  
Risk Evaluation ◽  
Operating Conditions ◽  
The Core ◽  
Supply Chain Finance ◽  
Enterprise Credit ◽  
Financial Credit ◽  
Core Enterprise

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.

A data-driven and network-aware approach for credit risk prediction in supply chain finance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Rishehchi Fayyaz ◽  
Mohammad R. Rasouli ◽  
Babak Amiri
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Data Set ◽  
Content Type ◽  
Network Measures ◽  
Credit Risks ◽  
Supply Chain Finance ◽  
Proposed Model

PurposeThe purpose of this paper is to propose a data-driven model to predict credit risks of actors collaborating within a supply chain finance (SCF) network based on the analysis of their network attributes. This can support applying reverse factoring mechanisms in SCFs.Design/methodology/approachBased on network science, the network measures of the actors collaborating in the investigated SCF are derived through a social network analysis. Then several supervised machine learning algorithms are applied to predict the credit risks of the actors on the basis of their network level and organizational-level characteristics. For this purpose, a data set from an SCF within an automotive industry in Iran is used.FindingsThe findings of the research clearly demonstrate that considering the network attributes of the actors within the prediction models can significantly enhance the accuracy and precision of the models.Research limitations/implicationsThe main limitation of this research is to investigate the applicability and effectiveness of the proposed model within a single case.Practical implicationsThe proposed model can provide a well-established basis for financial intermediaries in SCFs to make more sophisticated decisions within financial facilitation mechanisms.Originality/valueThis study contributes to the existing literature of credit risk evaluation by considering credit risk as a systematic risk that can be influenced by network measures of collaborating actors. To do so, the paper proposes an approach that considers network characteristics of SCFs as critical attributes to predict credit risk.

scholarly journals Evolutionary Game Analysis of Blockchain Technology Preventing Supply Chain Financial Risks

2021 ◽  
Vol 16 (7) ◽  
pp. 2824-2842
Author(s):  
Rui Sun ◽  
Dayi He ◽  
Huilin Su
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Financial Institutions ◽  
Evolutionary Game ◽  
Decisive Role ◽  
Financial Risks ◽  
Factors Affecting ◽  
Blockchain Technology ◽  
Supply Chain Finance ◽  
Before And After

Because of the risks existing in supply chain finance, taking accounts receivable factoring business as the research object, this paper uses the evolutionary game method to analyzes the factors affecting the decision-making of the participants in supply chain finance, constructs an evolutionary game model between small and medium-sized enterprises and financial institutions, and analyzes the mechanism of blockchain to solve the financial risks of the supply chain by comparing the changes of evolutionary stability strategies before and after the introduction of blockchain technology. This paper aims to reduce financing risks by analyzing the mechanism of blockchain technology in supply chain finance. It is found that, firstly, blockchain technology can reduce the credit risk of financial institutions and solve financing problem. Credit risk plays a decisive role in whether financial institutions accept financing business decisions. Blockchain technology can reduce the operational risk of financial institutions and improve the business income of financial institutions. Secondly, the strict regulatory environment formed by blockchain technology makes the default behavior of small and medium-sized enterprises and core enterprises in a high-risk state at all times. No matter the profit distribution proportion that small and medium-sized enterprises can obtain through collusion, they will not choose to default, which effectively solves the paradox that small and medium-sized enterprises cannot obtain loans from financial institutions despite the increased probability of compliance. Then, the evolutionary game between financial institutions and small and medium-sized enterprises is balanced in that financial institutions accept business applications, small and medium-sized enterprises abide by the contract, and the convergence effect is better. Therefore, blockchain technology not only reduces the financing risk of financial institutions but also helps to solve the financing problems of small and medium-sized enterprises.

scholarly journals On Credit Risk Contagion of Supply Chain Finance under COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wentao Chen ◽  
Zhenlin Li ◽  
Zhuoxin Xiao
Keyword(s):  
Supply Chain ◽  
Steady State ◽  
Credit Risk ◽  
Network Size ◽  
External Shocks ◽  
Credit Risks ◽  
Supply Chain Finance ◽  
Huge Impact ◽  
Credit Risk Contagion ◽  
The Impact

Existing research on credit risk contagion of supply chain finance pays more attention to the influence of network internal structure on the process of risk contagion. The spread of COVID-19 has had a huge impact on the supply chain, with a large number of enterprises experiencing difficulties in operation, resulting in increased credit risks in supply chain finance. Under the impact of the epidemic, this paper explores the transmission speed and steady state of credit risk when the supply chain finance network is affected by external impact so that we can have a more complete understanding of the ability of supply chain finance to resist risks. The simulation results show that external shocks of different degrees will increase the number of initial infected enterprises and lead to the increase in credit risk contagion speed but have no significant impact on network steady state; the speed of credit risk contagion is positively correlated with network complexity but not significantly affected by network size; core enterprises infected will increase the rate of credit risk contagion. The intensity of policy intervention has obvious curative effect on the risk caused by external shock. When the supply chain financial network is affected by external shocks, the intensity, time, and pertinence of policy response can effectively prevent the credit risk contagion.

scholarly journals Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China

2021 ◽  
Vol 13 (10) ◽  
pp. 5714
Author(s):  
Yubin Yang ◽  
Xuejian Chu ◽  
Ruiqi Pang ◽  
Feng Liu ◽  
Peifang Yang
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Logistic Model ◽  
Rapid Development ◽  
Sustainable Supply Chain ◽  
Key Factors ◽  
Credit Risks ◽  
Supply Chain Finance ◽  
Factors Influencing ◽  
Critical Issues

COVID-19 has created a strong demand for supply chain finance (SCF) for small and medium-sized enterprises (SMEs). However, the rapid development of SCF leads to more complex credit risks. How to effectively discriminate and manage SMEs to reduce credit risk has become one of the most critical issues in SCF. In addition, sustainable SCF (SSCF) has received increasing attention, and credit risk management is important to achieve SSCF. Therefore, it is significant to identify the key factors influencing the credit risk of SMEs and construct a prediction model to promote SSCF. This study uses the lasso-logistic model to identify factors influencing the credit risk of SMEs and to predict the credit risk of SMEs. The empirical results show that (i) the key factors influencing SMEs’ credit risk include six variables—the matching degree of order data, ratio of contract enforcement, number of contract defaults, degree of business concentration, and number of administrative penalties; and (ii) the lasso-logistic model can identify the key factors influencing credit risk and have a better prediction performance. Moreover, transaction credit and reputation supervision significantly influence the credit risk of SMEs.

scholarly journals The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining

2021 ◽  
Vol 13 (14) ◽  
pp. 7585
Author(s):  
Yunmei Liu ◽  
Shuai Zhang ◽  
Min Chen ◽  
Yenchun Wu ◽  
Zhengxian Chen
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Financial Institutions ◽  
Clustering Algorithm ◽  
Development Trend ◽  
Research Field ◽  
Trend Prediction ◽  
Blockchain Technology ◽  
Supply Chain Finance ◽  
The Government

Blockchain technology is the most cutting-edge technology in the field of financial technology, which has attracted extensive attention from governments, financial institutions and investors of various countries. Blockchain and finance, as an interdisciplinary, cross-technology and cross-field topic, has certain limitations in both theory and application. Based on the bibliometrics data of Web of Science, this paper conducts data mining on 759 papers related to blockchain technology in the financial field by means of co-word analysis, bi-clustering algorithm and strategic coordinate analysis, so as to explore hot topics in this field and predict the future development trend. The experimental results found ten research topics in the field of blockchain combined with finance, including blockchain crowdfunding, Fintech, encryption currency, consensus mechanism, the Internet of Things, digital financial, medical insurance, supply chain finance, intelligent contract and financial innovation. Among them, blockchain crowdfunding, Fintech, encryption currency and supply chain finance are the key research directions in this research field. Finally, this paper also analyzes the opportunities and risks of blockchain development in the financial field and puts forward targeted suggestions for the government and financial institutions.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Jia Liu ◽  
Shiyong Li ◽  
Xiaoxia Zhu
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Risk Analysis ◽  
Risk Prediction ◽  
Financial Institutions ◽  
Surface Runoff ◽  
Financial Risk ◽  
Supply Chain Finance ◽  
Runoff Model

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.

scholarly journals The Research of Agricultural SMEs Credit Risk Assessment Based on the Supply Chain Finance

2021 ◽  
Vol 275 ◽  
pp. 01061
Author(s):  
Zeping Tong ◽  
Shuo Yang
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Financial Services ◽  
Agricultural Modernization ◽  
Basic Industry ◽  
Supply Chain Finance ◽  
Rural Revitalization ◽  
Efficiency Factors ◽  
And Control ◽  
Stable Development

Agriculture is a basic industry that supports the construction and development of the national economy and plays an important role in promoting rural revitalization. And in the current post-COVID-19 era, agricultural SMEs have difficulty in obtaining the favours of financial institutions in normal lending due to their weak credit guarantee capabilities and high credit management costs. Difficulty in financing has become a bottleneck problem that plagues the development of enterprises and restricts the development of agricultural modernization. How to evaluate and control its credit risk is not only a major way to solve the financing difficulties of agricultural SMEs, but also the basis for the stable development of supply chain financial services. This paper analyzes three typical financing modes of agricultural SMEs from the perspective of supply chain finance, and takes the agricultural SMEs in the New OTC Market as an example to construct a Logistic model, and uses factor analysis to effectively predict the credit risk of supply chain finance. The results show that the operational efficiency factors, growth factors and related core corporate profitability of agricultural SMEs financing enterprises significantly affect their credit risk. After testing, the model is highly accurate in predicting the financing risks of agricultural SMEs.

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