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

Yao-Zhi Xu,Lin-Yue Wang,Ying Hua,Jian-Lin Zhang

doi:10.3390/su11195521

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.

Highlights

Credit risk evaluation is the foundation in establishing trust between sellers and buyers in an e-commerce environment
Our paper studies the process of credit risk evaluation and how advances in technology are changing this process
The combination model would improve the credit risk prediction accuracy ratio, and overcome the shortcoming of a single artificial intelligence (AI) model’s lack of interpretability. It can serve as a reference for e-commerce platform promoting optimum credit risk evaluation model to improve e-commerce transaction environment and for buyers and investors making decisions. (2) we demonstrate that the credit risk prediction performance of decision tree— artificial neural network (ANN) model is better than that of decision tree—logistic regression (LR), decision tree—dynamic Bayesian network (DBN) and single ANN, LR, DB models in evaluating the credit risk of sellers on Taobao platform

Summary

Introduction

Credit risk evaluation is the foundation in establishing trust between sellers and buyers in an e-commerce environment. It is an important part of e-commerce industrial chain, and the key ingredient for the sustainability of the financial market. In virtual e-commerce transactions, the information asymmetry between sellers and buyers results in increased uncertainty and transaction risk. While the e-commerce market is still rapidly developing, the problem of credit deficiency is becoming one of the most important bottlenecks for the sustainable development of e-commerce. Without effective credit risk evaluation the ‘lemon problem’ occurs in platform mediated network market. Credit deficiency undermines the orderliness, fairness and competitiveness of the financial market, which is extremely harmful to the healthy development of e-commerce

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Discussion

Conclusion