[1]朱菁婕,吴怀岗.企业信用评估指标体系及信用评估模型研究[J].南京师范大学学报(工程技术版),2020,20(03):081-86.[doi:10.3969/j.issn.1672-1292.2020.03.013]
　Zhu Jingjie,Wu Huaigang.Research on the Index System and EvaluationModel of Enterprise Credit Evaluation[J].Journal of Nanjing Normal University(Engineering and Technology),2020,20(03):081-86.[doi:10.3969/j.issn.1672-1292.2020.03.013]
点击复制

企业信用评估指标体系及信用评估模型研究
分享到：

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
20卷
期数:
2020年03期
页码:
081-86
栏目:
管理科学与工程
出版日期:
2020-09-15

文章信息/Info

Title:
Research on the Index System and EvaluationModel of Enterprise Credit Evaluation
文章编号:
1672-1292(2020)03-0081-06
作者:
朱菁婕; 吴怀岗
南京师范大学计算机科学与技术学院,江苏 南京 210023
Author(s):
Zhu Jingjie; Wu Huaigang
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
关键词:
企业信用评估; 信用指标体系; 信用评估模型
Keywords:
enterprise credit evaluation; credit index system; credit evaluation model
分类号:
F832.4
DOI:
10.3969/j.issn.1672-1292.2020.03.013
文献标志码:
A
摘要:
针对企业的信用评估,基于已有研究,引入企业财务指标和非财务指标,使用机器学习分类方法构建信用评估模型,并对几种方法的分类准确率进行了比较分析. 实验结果表明,该信用评估指标体系可行,随机森林方法在该指标体系上的分类效果最好. 同时,优化了分类效果较差的多层感知器,提升了分类准确率.
Abstract:
As for enterprise credit evaluation,this paper introduces financial indexes and non-financial indexes based on previous research. Machine learning classification methods are used to build credit evaluation models,and the classification accuracy rates of several methods are compared and analyzed. The experimental results show that the credit evaluation index system is feasible,and that the random forest method has the best classification effect on the index system. At the same time, the multi-layer perceptron with poor classification effect is optimized,and the classification accuracy is improved.

参考文献/References:

[1] STANDARD & POOR’S. General criteria:principles of credit ratings[EB/OL].(2011-02-16). http://www.standardandpoors.com/prot/ratings/articles/en/us/?articleType=HTML&assetID=1245366284668.
[2]MOODY’S. Global business & consumer service industry rating methodology[EB/OL].(2013-10-13). https://www.moodys.com/researchdocumentcontentpage.aspx?docid=PBC_127102.
[3]MOODY’S. Global retail industry[EB/OL].(2011-06-30). https://www.moodys.com/researchdocumentcontentpage.aspx?docid=PBC_133476.
[4]MOODY’S. Global software industry[EB/OL].(2012-10-09). https://www.moodys.com/researchdocumentcontentpage.aspx?docid=PBC_142367.
[5]王丽英. 关于印发《中国建设银行小企业客户债信评级办法》的通知:建总发[2009]101号[R]. 北京:中国建设银行,2009.
[6]赵亚,李田,苑泽明. 基于随机森林的企业信用风险评估模型研究[J]. 财会通讯,2017(29):110-114.
[7]曹裕,陈霞,刘小静. 违约距离视角下的开发性金融信用风险评估[J]. 财经理论与实践,2017,38(209):14-19.
[8]王海峰,张晓妮,石宝峰. 基于模糊聚类的 P2P 网贷个人信用评估模型及应用[J]. 浙江金融,2017(10):19-26.
[9]王克敏,罗艳梅. 中国上市公司对外担保与财务困境研究[J]. 吉林大学社会科学学报,2006,46(5):106-113.
[10]王楚萱,陶宝山,吴晨. 对外担保会影响商业信用融资吗?[J]. 财会通讯,2018(18):118-122.
[11]付永贵,朱建明. 基于大数据的网络供应商信用评估模型[J]. 中央财经大学学报,2016(8):74-83.
[12]王凯,黄世祥. 行业内中小企业信用评估模型及应用[J]. 数学的实践与认识,2008,38(4):64-77.
[13]NIKOLIC N,ZARKIC J N,STOJANOVSKI D,et al. The application of brute force logistic regression to corporate credit scoring models:evidence from Serbian financial statement[J]. Expert Systems with Applications,2013,40(15):5932-5944.
[14]陈永明,周龙,李双红. 基 AHP和DEMATEL方法的农户信用评级研究[J]. 征信,2012(5):20-24.
[15]GUO W,TONG K N,SHAO H Y,et al. Small and medium-sized enterprises multi-service agent credit rating system construction under cloud manufacturing mode based on RS and AHP[J]. Computer Integrated Manufacturing Systems,2013,19(9):2340-2347.
[16]CHEN W,GAO M,YANG Y. Dynamic credit supervision system of agent construction in public investment project and process design[J]. Advanced Management Science,2013,22(1-2):12.
[17]刘骅,张婕. 互联网金融信用风险预警与审计治理策略研究:以江苏省P2P网贷平台为例[J]. 南京财经大学学报,2017(4):59-66.
[18]陈晓红,杨志慧. 基于改进模糊综合评价法的信用评估体系研究[J]. 中国管理科学,2015,23(1):146-153.
[19]刘京礼,李建平,徐伟宣,等. 信用评估中的鲁棒赋权自适应Lp最小二乘支持向量机方法[J]. 中国管理科学,2010,18(5):28-33.
[20]邵蔚. 对P2P平台信用风险的评估与预测:基于决策树模型[J]. 北方经贸,2017(9):103-104.
[21]郑杰. 基于随机森林模型的P2P借款人信用评估研究[D]. 哈尔滨:哈尔滨工业大学,2017.
[22]ODOM M D,SHARDA R. A neural network model for bankruptcy prediction[C]//IJCNN International Joint Conference on Neural Networks. Diego,USA:Browman C P,Goldstein L,1990:163-168.
[23]叶夏菁. 基于BP神经网络与半监督学习的网贷平台信用评估模型[D]. 浙江大学,2015.
[24]孙庆文,张琼琼,仇静莉,等. 基于不同信息获取量的赊销决策风险度判别模型[J]. 系统工程理论与实践,2012,32(1):41-48.
[25]BAESENS B,GESTEL V T,VIAENE S. Benchmarking state-of-art classification algorithms for credit scoring[J]. Journal of the Operation Research Society,2003,54(6):627-635.
[26]ZHANG Q,WANG J,LU A G. An improved SMO algorithm for financial credit risk assessment-evidence from China’s banking[J]. Neurocomputing,2018,272:314-325.
[27]肖斌卿,柏巍,姚瑶,等. 基于LS-SVM的小微企业信用评估研究[J]. 审计与经济研究,2016,31(6):102-111.
[28]赵亚,李田,苑泽明. 基于随机森林的企业信用风险评估模型研究[J]. 财会通讯,2017(29):110-114.
[29]PALEOLOGO G,ELISSEEFF A,ANTONINI G. Subagging for credit scoring models[J]. European Journal of Operational Research,2010,201(2):490-499.
[30]肖进,薛书田,黄静,等. 客户信用评估半监督协同训练模型研究[J]. 中国管理科学,2016,24(6):124-130.
[31]黄静,薛书田,肖进. 基于半监督学习的客户信用评估集成模型研究[J]. 软科学,2017,31(7):131-134.
[32]LEGEN D. AUC越大,正确率就越高?[EB/OL].(2018-01-15). https://blog.csdn.net/legendavid/article/details/79063044.

备注/Memo

备注/Memo:
收稿日期:2019-12-09.
通讯作者:吴怀岗,博士,副教授,研究方向:管理信息系统、大数据分析. E-mail:05324@njnu.edu.cn
更新日期/Last Update: 2020-09-15