基于贝叶斯网络的中国电子商务信用风险因素研究
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摘要
针对电子商务信用问题可能会导致的电子商务发展缓慢、电子商务市场秩序混乱以及电子商务产业崩溃等风险事件,应用贝叶斯网络理论构建中国电子商务信用风险网络模型,分别分析电子商务信用风险的敏感性因素与关键因素对风险事件的影响程度。运用GeNie仿真软件求得结果:最关键的信用风险因素是技术诈骗率提高、经济效率降低以及国家监管力度不足等三个因素,敏感性因素为新者难做、交易者的交易意愿降低、技术诈骗率提高、经济效率降低以及信息不对称等五个因素。研究认为信用风险关键因素的变化会波及信用风险敏感因素的变动,进而引发风险事件的产生。
Facing the risk events of slow development of e-commerce, disorder of e-commerce market and collapse of e-commerce industry which are all caused by e-commerce credit issues, this paper uses the Bayesian network theory to construct the credit risk network model of e-commerce in China, in order to analyze the impact of both sensitivity factors and key factors on risk events. The results are shown by using GeNie simulation software: three crucial credit risk factors are higher technical fraud, lower economic efficiency and lack of national regulatory effort; the five sensitivity factors are uneasy new comers, reduction of transaction willingness, higher technical fraud, lower economic efficiency and information asymmetry. The study shows that the change of the key factors of credit risk will result in the change of sensitivity factors in credit risk, thus leading risk events to happen at last.
Facing the risk events of slow development of e-commerce, disorder of e-commerce market and collapse of e-commerce industry which are all caused by e-commerce credit issues, this paper uses the Bayesian network theory to construct the credit risk network model of e-commerce in China, in order to analyze the impact of both sensitivity factors and key factors on risk events. The results are shown by using GeNie simulation software: three crucial credit risk factors are higher technical fraud, lower economic efficiency and lack of national regulatory effort; the five sensitivity factors are uneasy new comers, reduction of transaction willingness, higher technical fraud, lower economic efficiency and information asymmetry. The study shows that the change of the key factors of credit risk will result in the change of sensitivity factors in credit risk, thus leading risk events to happen at last.
引文
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[2] 曾勇.电子商务信用风险机理研究[D].武汉理工大学,2005.
[3] 邬建平.基于粗糙集和支持向量机的电子商务信用风险分类[J].数学的实践与认知,2016,46(13):87-92.
[4] Jones K,Leonard L N K.Trust in consumer to consumer electronic commerce[J].Information & Management,2008,45(2):88-95.
[5] 刘兆君,毛荐其.电子商务信用风险的F积分综合评价[J].北京交通大学学报:社会科学版,2008,7(4):70-74.
[6] 余乐安.基于最小二乘近似支持向量回归模型的电子商务信用风险预警[J].系统工程理论与实践,2012,32(3):508-514.
[7] 朱小宗,张宗益,耿华丹.信用风险度量方法与建模研究[J].系统工程学报,2006,21(6):561-567.
[8] Trucco P,Gagno E,Ruggeri F.A bayesian belief network modeling of organizational factors in risk analysis:a case study in maritime transportation[J].Reliability Engineering and System Safety,2008(93):823- 834.
[9] Nordg D E,Sand K.Application of bayesian networks for risk analysis of MV air insulated switch operation[J].Reliability Engineering and System Safety,2010(95):1358-1366.
[10] Cuaya G,Mueoz-Melendez A,Carrera L N,et al.A dynamic bayesian network for estimating the risk of falls from real gait data[J].Medical & Computing,2013,51(1-2):29-37.
[11] Heisca S,Kühn R.Derivatives and credit contagion in interconnected networks[J].The European Physica Journal:B,2012,85(4):1-19.