scholarly journals Compressed Sensing and its Applications in Risk Assessment for Internet Supply Chain Finance Under Big Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 53182-53187 ◽  
Author(s):  
Xiumei Lyu ◽  
Jiahong Zhao
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Supply Chain ◽  
Compressed Sensing ◽  
Supply Chain Finance

scholarly journals Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective

Logistics ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Hisham Alidrisi
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Multicriteria Decision Making ◽  
Analytic Network Process ◽  
Assessment Model ◽  
Self Assessment ◽  
Interpretive Structural Modeling ◽  
Supply Chain Finance ◽  
Network Process ◽  
Order Preference

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.

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 Risk Evaluation and Control of Supply Chain Finance

2018 ◽  
Vol 6 (1) ◽  
pp. 21
Author(s):  
Ying Yu Du ◽  
Jun Bin Zhong ◽  
Zi Yue Su ◽  
Xin Peng Yang ◽  
Yi Lan Yao
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Key Factors ◽  
Supply Chain Risk ◽  
Analytic Hierarchy ◽  
Industry Chain ◽  
Supply Chain Finance ◽  
Financial Index ◽  
And Control ◽  
Hierarchy Process

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.

scholarly journals Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination

2020 ◽  
Vol 16 (1) ◽  
pp. 155014772090363 ◽  
Author(s):  
Ying Liu ◽  
Lihua Huang
Keyword(s):  
Risk Assessment ◽  
Support Vector Machine ◽  
Supply Chain ◽  
Credit Risk ◽  
Financial Analysis ◽  
Learning Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Machine Model ◽  
Supply Chain Finance

Recently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management. However, noise may increase the complexity of the algorithm building and destroy the performance of classifier. In our work, we propose an ensemble support vector machine model to solve the risk assessment of supply chain finance, combined with reducing noises method. The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. Then, we obtained the final classification results by combining finally individual prediction through AdaBoosting algorithm on the new sample set. Some experiments are applied on supply chain financial analysis of China’s listed companies. Results indicate that the credit assessment accuracy can be increased by applying this approach.

scholarly journals Analysis on Internet Financial Business and Construction of Credit System

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Anzhi Yang
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Evaluation System ◽  
Mobile Internet ◽  
The Internet ◽  
Credit Evaluation ◽  
Supply Chain Finance ◽  
Industrial Chain ◽  
Internet Finance ◽  
Credit Evaluation System

Internet finance is a new emerging financial model, using the Internet as a platform, big data and cloud computing as the basis. Supply Chain Finance is the easiest way to enter Internet finance. The third-party companies or institutions can invest in Internet financial companies by integrating their industrial chain practices into designing the financial products to reduce credit costs and improve safety. At the same time, it will increase mobile Internet, big data and operational services. Also, it can make full use of the Internet financial platform to provide value-added services for higher and lower enterprise and consolidate the core status of the company in the industrial chain. However, an important issue that needs to be concerned during developing Supply Chain Finance is the construction of a system for credit evaluation. Due to the lack of a unified credit evaluation system, the development of the existing Supply Chain Financial companies suffers from difficulties. Many newly launched companies have difficulties operating due to the lack of a credit evaluation system. Therefore, proper and effective credit indicators are essential for the development of enterprises under Internet finance. From the micro perspective, it is conducive for enterprises to improve their credit under the constraints of indicators, and it can solve the problem of capital raising; from the macro perspective, it is conducive to the standardized development of China’s Internet finance and promotes the comprehensive economic development. Based on this, analyzing the model of Internet financial business and developing an enterprise’s credit index system is beneficial to the development of China’s Internet finance.

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