Supply Chain Credit Evaluation Mechanism Integrating Federated Learning and Blockchain

2021 ◽  
pp. 1471-1480
Author(s):  
Qi Ma ◽  
Huifeng Yang ◽  
Dong Wang ◽  
Wei Liu ◽  
Shaoyong Guo
Keyword(s):  
Supply Chain ◽  
Credit Evaluation ◽  
Evaluation Mechanism

Research on Mode of after-Sale Reverse Logistics Operation of Auto Maintenance Industry Based on Time Window

2011 ◽  
Vol 121-126 ◽  
pp. 4635-4639
Author(s):  
Hao Hao ◽  
Xing Gen Wu ◽  
Hong Yu Li
Keyword(s):  
Supply Chain ◽  
Reverse Logistics ◽  
High Speed ◽  
Time Window ◽  
Auto Industry ◽  
Road Transport ◽  
Motor Vehicles ◽  
Safety Testing ◽  
Internal Operations ◽  
Evaluation Mechanism

The author maintains that, with the quick development of domestic auto industry, the auto maintenance industry is progressing with high speed accordingly. The auto maintenance enterprise are inclined to focus on sales and forward supply chain operation, however, lacking of efficient resource and systematic management into reverse logistics operation, as lead to a few potential risks. In order to avoid and control the risks, the auto maintenance companies need to closely cooperate with channel supply chain partners, meanwhile build up the mode of reverse logistics operation with timing efficiency orientation. This mode consists of eight sub-system modules: organization structure, network planning and layout, reverse logistics operation procedure, time window evaluation mechanism, automatic replenishment system, reverse forecasting system, Kan-ban message system as well as disposal & reuse system. Furthermore, the article makes analysis of the internal operations and external relevance of these eight sub-systems. In recent years, with the overall opening up of road transport market and the rapid growth in domestic economy, the total number of China’s motor vehicles is entering a high-speed growth channel, and “the car goes to the country” policy further promotes the auto industry. Under the situation of quick development, the auto maintenance industry is developing by 10% to 15% each year as a young profession. According to the statistics, there are over 380,000 auto maintenance enterprises in our country at the moment, and various types of auto safety testing stations are built around one after another. The intense competition among auto maintenance manufacturing enterprises does not only focus on product quality, but also on the after-market reverse logistics. According to the statics of Gartner, 70 percent of resale is related with sales service, and 60 percent of resale is related with after market. However, except for few leading business enterprises which have realized the direct influence and value contribution of after-service reverse logistics to profit increase and customer loyalty and take action to pioneer the profit territory in the after-sale reverse logistics, the ‘price war’ is still the main competition method which the majority of enterprises adopt in the market.

Forward looking vs backward looking

2017 ◽  
Vol 7 (2) ◽  
pp. 228-248 ◽  
Author(s):  
Yanyan Gao ◽  
Jun Sun ◽  
Qin Zhou
Keyword(s):  
Default Risk ◽  
Evaluation System ◽  
Content Type ◽  
Credit Evaluation ◽  
Additional Information ◽  
Ex Post ◽  
P2p Lending ◽  
Evaluation Mechanism ◽  
Credit Evaluation System ◽  
Forward Looking

Purpose The purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com. Design/methodology/approach The current credit valuation systems are classified into the forward-looking mechanism, which judges the borrowers’ credit levels based on their uploaded information, and the backward-looking mechanism, which judges the borrowers’ credit levels based on their historical repayment performance. Probit models and Tobit models are used to examine the effectiveness of credit evaluation mechanisms. Findings The results show that only the “hard” information reflecting borrowers’ credit ability can explain the default risk on the platform under the forward-looking credit evaluation mechanism. The backward-looking credit evaluation mechanism (BCEM) based on the repeated borrowings produces both promise-enhancing and “fishing” incentives and thus fails to explain the default risk, and weakens the effectiveness of forward-looking credit indicators in explaining the default risk because it encourages borrowers to invest in forging forward-looking credit indicators. Additional information such as the interest rate and the repayment periods reveals borrowers’ credit and thus can also be used as a predictor of borrowers’ default risk. Practical implications The findings suggest that current ex ante screening based on the information collected from the borrowers or repeated borrowings is inadequate to control the default risk in P2P lending markets and thus needs be improved. Ex post monitoring and sharing on defaulter’s information should be strengthened to increase the default cost and thus to deter potential bad borrowers. Originality/value To the authors’ knowledge, this is the first paper classifying the credit evaluation system in online P2P lending market into the forward-looking type and the backward-looking type, which is important since they provide different incentives to borrowers. The paper also investigates and provides evidence on the promise-enhancing and “fishing” incentives of BCEMs.

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.

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