Dynamic Credit Risk Evaluation Method for E-Commerce Sellers Based on a Hybrid Artificial Intelligence Model

Credit risk evaluation is important for e-commerce platforms, due to the uncertainty and transaction risk associated with buyers and sellers. Moreover, it is the key ingredient for the development of the e-commerce ecosystem and sustainability of the financial market. The main objective of this pape...

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Bibliographic Details
Published inSustainability Vol. 11; no. 19; p. 5521
Main Authors Xu, Yao-Zhi, Zhang, Jian-Lin, Hua, Ying, Wang, Lin-Yue
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2019
Subjects
Artificial intelligence
Bankruptcy
Bayesian analysis
Big Data
Consumers
Credibility
Decision making
Default
Electronic commerce
Evaluation
Learning theory
Literature reviews
Mathematical models
Neural networks
Product quality
Risk assessment
Securities markets
Small & medium sized enterprises-SME
Small business
Sustainability
Sustainable development
China
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Summary:Credit risk evaluation is important for e-commerce platforms, due to the uncertainty and transaction risk associated with buyers and sellers. Moreover, it is the key ingredient for the development of the e-commerce ecosystem and sustainability of the financial market. The main objective of this paper is to develop an effective and user-friendly system for seller credit risk evaluation. Three hybrid artificial intelligent models, including (1) decision tree—artificial neural network (ANN), (2) decision tree—logistic regression, and (3) decision tree—dynamic Bayesian network have been investigated. The models were trained using sellers credit cases from Taobao, which has 609 cases, and each case had 23 categorical and numerical attributes. The results suggest that the combination of decision tree—ANN provides the highest accuracy, which can promote healthy and fast transactions between buyers and sellers on the platforms. This model is regarded as a powerful tool that allows us to build an advanced credit risk evaluation system, and meet the requirements of the platform transaction mode to be dynamic and self-learning—which will ultimately contribute to the sustainable development of the e-commerce ecosystem. The empirical results can serve as a reference for e-commerce platforms promoting an optimum credit risk evaluation model to improve e-commerce transaction environment and for buyers and investors making decisions.
ISSN:2071-1050
2071-1050
DOI:10.3390/su11195521