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.

scholarly journals Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain

2018 ◽  
Vol 15 (8) ◽  
pp. 1627 ◽  
Author(s):  
Dianhui Mao ◽  
Fan Wang ◽  
Zhihao Hao ◽  
Haisheng Li
Keyword(s):  
Supply Chain ◽  
Food Supply ◽  
Evaluation System ◽  
Short Term Memory ◽  
Support Vector ◽  
Sufficient Information ◽  
Food Supply Chain ◽  
Credit Evaluation ◽  
Supervision And Management ◽  
Credit Evaluation System

The food supply chain is a complex system that involves a multitude of “stakeholders” such as farmers, production factories, distributors, retailers and consumers. “Information asymmetry” between stakeholders is one of the major factors that lead to food fraud. Some current researches have shown that applying blockchain can help ensure food safety. However, they tend to study the traceability of food but not its supervision. This paper provides a blockchain-based credit evaluation system to strengthen the effectiveness of supervision and management in the food supply chain. The system gathers credit evaluation text from traders by smart contracts on the blockchain. Then the gathered text is analyzed directly by a deep learning network named Long Short Term Memory (LSTM). Finally traders’ credit results are used as a reference for the supervision and management of regulators. By applying blockchain, traders can be held accountable for their actions in the process of transaction and credit evaluation. Regulators can gather more reliable, authentic and sufficient information about traders. The results of experiments show that adopting LSTM results in better performance than traditional machine learning methods such as Support Vector Machine (SVM) and Navie Bayes (NB) to analyze the credit evaluation text. The system provides a friendly interface for the convenience of users.

Internet Financial Credit Evaluation for the Small and Medium Enterprises

2014 ◽  
Vol 989-994 ◽  
pp. 5075-5077
Author(s):  
Yi Qing Lu
Keyword(s):  
Big Data ◽  
Credit Risk ◽  
Information Asymmetry ◽  
Financial Institutions ◽  
Evaluation System ◽  
Small And Medium Enterprises ◽  
Credit Evaluation ◽  
Financial Credit ◽  
Medium Enterprises ◽  
Credit Evaluation System

In this paper, a credit evaluation system based big-data is designed to change the information asymmetry between the finance institutions and enterprises, reduce the credit risk of internet financial institutions and investors, by utilizing the information and technology advantages. The research objective of this project have important theoretical and application value to the development of small and medium-sized enterprises (SME) credit evaluation system.

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.

Research on Credit Evaluation System of Electricity Retail Companies

2021 ◽  
Author(s):  
Zhiwei Ying ◽  
Tao Yu ◽  
Yupeng Huang ◽  
Hanfu Wang ◽  
Dunnan Liu ◽  
...  
Keyword(s):  
Evaluation System ◽  
Credit Evaluation ◽  
Credit Evaluation System

Credit Risk Management for College Students: An Analysis of Influencing Factors Based on 5C Approach

2021 ◽  
Vol 12 (1) ◽  
pp. 105-105
Author(s):  
Xiang Zou ◽  
Jinting Zhao ◽  
Yun Tong
Keyword(s):  
College Students ◽  
Risk Management ◽  
Credit Risk ◽  
Evaluation System ◽  
Least Squares Method ◽  
Credit Risk Management ◽  
Credit Evaluation ◽  
Personal Credit ◽  
Depth Analysis ◽  
Credit Evaluation System

This paper focuses on the construction of college students' credit evaluation system and credit risk management under the background of big data. Firstly, based on the 5C approach, this paper evaluates the personal credit of college students from 5 dimensions and 24 indicators, which finally contribute to the establishment of the credit evaluation system for college students. Then, the partial least squares method is used to build the structural equation model to evaluate the effectiveness of the credit evaluation system for college students. According to the in-depth analysis of PSL-SEM, the factors that affect the credit risk of college students are effectively evaluated, and it has contributed to the establishment and improvement of the credit system of college students. Keywords: Personal Credit, Credit Evaluation, Credit Risk, 5C Approach, PLS-SEM.

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.

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