scholarly journals Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining

2020 ◽  
Author(s):  
Shuo Yang ◽  
Zhiqiang Zhang ◽  
Jun Zhou ◽  
Yang Wang ◽  
Wang Sun ◽  
...  
Keyword(s):  
Supply Chain ◽  
Risk Analysis ◽  
Financial Institutions ◽  
Financial Risk ◽  
Vital Role ◽  
Analysis Framework ◽  
Chain Graph ◽  
Related Data ◽  
Related Information ◽  
Relationship Of

Small and Medium-sized Enterprises (SMEs) are playing a vital role in the modern economy. Recent years, financial risk analysis for SMEs attracts lots of attentions from financial institutions. However, the financial risk analysis for SMEs usually suffers data deficiency problem, especially for the mobile financial institutions which seldom collect credit-related data directly from SMEs. Fortunately, although credit-related information of SMEs is hard to be acquired sufficiently, the interactive relationships between SMEs, which may contain valuable information of financial risk, is usually available for the mobile financial institutions. Finding out credit-related relationship of SME from massive interactions helps comprehensively model the SMEs thus improve the performance of financial risk analysis. In this paper, tackling the data deficiency problem of financial risk analysis for SMEs, we propose an innovative financial risk analysis framework with graph-based supply chain mining. Specifically, to capture the credit-related topology structural and temporal variation information of SMEs, we design and employ a novel spatial-temporal aware graph neural network, to mine supply chain relationship on a SME graph, and then analysis the credit risk based on the mined supply chain graph. Experimental results on real-world financial datasets prove the effectiveness of our proposal for financial risk analysis for SMEs.

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.

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

2020 ◽  
pp. 1-11
Author(s):  
Fan Xuan
Keyword(s):  
Supply Chain ◽  
Financial Institutions ◽  
Financial Services ◽  
Index System ◽  
Financial Risk ◽  
Control Measures ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Supply Chain Finance ◽  
Strong Control

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.

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

2020 ◽  
Vol 214 ◽  
pp. 03032
Author(s):  
Weiting Sun ◽  
Puxue Shen
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Financial Institutions ◽  
Financial Risk ◽  
Large Scale ◽  
Theory And Practice ◽  
Privacy And Security ◽  
Internet Applications ◽  
Supply Chain Finance ◽  
Big Data Technology

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.

Learning from Zhenjiang Water Pollution Accident

2013 ◽  
Vol 726-731 ◽  
pp. 1879-1882 ◽  
Author(s):  
Xing Hua Fan ◽  
Yan Shi ◽  
Xue Bing Han
Keyword(s):  
Water Pollution ◽  
Risk Analysis ◽  
Vital Role ◽  
Accident Risk ◽  
Analysis Framework ◽  
Key Factors ◽  
Local Society ◽  
Factors Affecting ◽  
Information Release ◽  
The Government

The accident that occurred in Zhenjiang in Feburary 2012 caused chaos in local society and international attention. It was caused by chemical leak from cargo ship but the weak communication between the government and the community played a vital role. This paper analyzes the accident scenario using the risk analysis framework, determines key factors influenced the occurrence of the basic events. By comparing study we conclude that the key factors affecting such water pollution accidents includes timely information release, perfect water supply system and strict legal system.Keywords: water pollution accident; environmental accident; risk analysis; government’s role; information release

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

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Keyu Wang ◽  
Fuhai Yan ◽  
Yangjingjing Zhang ◽  
Yunlong Xiao ◽  
Lexi Gu
Keyword(s):  
Supply Chain ◽  
Financial Institutions ◽  
Smart City ◽  
Evaluation System ◽  
Financial Risk ◽  
Evaluation Model ◽  
Stable Operation ◽  
Financial Risks ◽  
Supply Chain Finance ◽  
Promethee Method

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.

Country Risk Analysis of Bangladesh

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Zahidur Rahman ◽  
Jannatul Ferdous Bristy
Keyword(s):  
Risk Analysis ◽  
Financial Risk ◽  
Political Risk ◽  
Multinational Companies ◽  
Country Risk ◽  
Low Risk ◽  
Primary Data ◽  
Policy Makers ◽  
Financial Factors ◽  
Political Economic

In the endeavor of conquering the worlds consumers, multinational companies face enormous risks. Such risks may arise from different political, economic, and financial factors. These factors are commonly referred to country risk as a whole. Focusing Bangladesh in this regard, objective of this study is to find out the level of country risk in terms of political, economic, and financial riskiness. Analysis of country risk has been done using an internationally recognized methodology named International Country Risk Guide (ICRG). For political risk analysis, primary data has been collected from 20 journalists, bureaucrats and policy makers, business persons, corporate professionals, and academicians with a structured closed-ended questionnaire. Results indicate that Bangladesh is in high risk position in terms of political risk, low risk position in terms of economic risk and very low risk position in terms of financial risk. Compositely, Bangladesh has been found to be a moderately risky country for investment.

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.

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