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小微企业信用评价模型的中外比较及完善 被引量:6

The International Comparison and Improvement of Small and Micro Businesses Credit Evaluation Model
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摘要 为提高商业银行评价小微企业信用的有效性,在提高支持小微企业力度的同时降低商业银行的贷款风险,我们采用比较分析法,选择了美国、日本、印度的小微企业信用评价模型作为参照系,对比并揭示我国现行的小微企业信用评价模型存在信用评价分值的确定过于依赖主观判断、评价标准过于笼统僵化、反映企业领导者素质及企业商誉的指标权重过低等缺陷。建议采取细分定量指标标准、设定合理的定性指标、设立能充分反映风险的评价指标权重、建立完善的全国性征信系统、研发针对小微企业特点的信用评价标准等措施,完善中国的小微企业信用评价模型。 In order to improve the effectiveness of commercial banks to evaluate small and micro businesses and to reduce the loan risk of commercial banks,the authors use the method of comparative analysis and choose the small and micro businesses credit evaluation model in American,Japan and India as reference. The comparison shows the defects of the small and micro businesses credit evaluation model in China,such as the too much subjective judgment,the too general and rigid evaluation standards and the too low weights of indexes reflecting the enterprise leader's quality and the enterprise goodwill. The authors also put forwards that we should adopt subdivision index standard,set reasonable qualitative index,determine the weight of evaluation index reflecting risk,establish the national credit reporting system and carry out research on the credit evaluation standards of small and micro businesses to improve the credit evaluation model for small and micro enterprise in China.
出处 《中国流通经济》 CSSCI 北大核心 2014年第9期74-79,共6页 China Business and Market
关键词 小微企业 信用评价模型 商业银行 中外比较 small and micro businesses credit evaluation models commercial banks international comparison
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  • 1孙常翔,李乃文,殷雪婷.信用概念的经济学分析[J].辽宁工程技术大学学报(社会科学版),2005,7(1):39-40. 被引量:3
  • 2刘胥影,吴建鑫,周志华.一种基于级联模型的类别不平衡数据分类方法[J].南京大学学报(自然科学版),2006,42(2):148-155. 被引量:23
  • 3付杰.信用的一般认识和经济学意义[J].黑龙江金融,2007(3):19-20. 被引量:1
  • 4Marx V. Biology: The Big Challenges of Big Da- ta[J]. Nature, 2013, 498(7453): 255-260.
  • 5Plummet D C, Bittman T J, Austin T, et al. Cloud Computing: Defining and Describing an Emerg- ing Phenomenon[J]. Gartner, 2008.
  • 6Dunham M H. Data Mining: Introductory and Advanced Topics[M]. Pearson Education India, 2006.
  • 7Cios K J, Pedryez W, Swiniarski R W. Data Mining and Knowledge Discovery[M]. Springer US, 1998.
  • 8Kass G V. An Exploratory Technique for In- vestigating Large Quantities of Categorical Data[J]. Ap- plied Statistics, 1980, 29(2): 119-127.
  • 9Quinlan J R. Induction of Decision Trees[J]. Machine Learning, 1986, 1(1): 81-106.
  • 10Loh W Y, Shih Y S. Split Selection Methods for Classification Trees[J]. Statisticasinica, 1997, 7(4): 815-840.

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