scholarly journals The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

2017 ◽  
Vol 05 (01) ◽  
pp. 20-30
Author(s):  
Yueliang Su ◽  
Baoyu Zhong
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Supply Chain ◽  
Credit Risk ◽  
Assessment Model ◽  
Multi Criteria Decision Making ◽  
Risk Assessment Model ◽  
Supply Chain Finance ◽  
Variable Weight ◽  
Decision Making Model

AHP-Driven Knowledge Leakage Risk Assessment Model

2020 ◽  
pp. 218-237 ◽  
Author(s):  
Haley Wing Chi Tsang ◽  
Wing Bun Lee ◽  
Eric Tsui
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Intellectual Capital ◽  
Value Chain ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Analytic Hierarchy ◽  
Job Knowledge ◽  
Knowledge Leakage ◽  
Hierarchy Process

Intellectual Capital (IC) is becoming more widely understood by the academic and business communities, especially its important role in value creation of an organization. However, few people are aware that IC, if not managed properly, may also pose threats, sometime serious, to an organization. Knowledge leakage from an organization, for example, may come about when an experienced employee leaves for another job. Knowledge leakage is pervasive throughout an organization but is seldom noticed until the consequence is felt. This intellectual capital risk has to be systematically and effectively identified, assessed and controlled in the whole value chain of an organization. An AHP (Analytic Hierarchy Process) based multi-dimensional decision making and assessment model is developed to determine knowledge leakage risk in an organization.

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.

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