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

Yao-Zhi Xu (), Jian-Lin Zhang (), Ying Hua () and Lin-Yue Wang ()
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Yao-Zhi Xu: School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China
Jian-Lin Zhang: Alibaba Business School, Hangzhou Normal University, Hangzhou 311121, China
Ying Hua: School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China
Lin-Yue Wang: School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China

Sustainability, 2019, vol. 11, issue 19, 1-17

Abstract: 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.

Keywords: dynamic credit risk evaluation; hybrid artificial intelligent model; decision tree; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2019
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