计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 116-118.doi: 10.11896/jsjkx.200400017

• 人工智能 • 上一篇    下一篇

基于逻辑回归的金融风投评分卡模型实现

边玉宁, 陆利坤, 李业丽, 曾庆涛, 孙彦雄   

  1. 北京印刷学院 北京 102600
  • 出版日期:2020-11-15 发布日期:2020-11-17
  • 通讯作者: 边玉宁(774200483@qq.com)
  • 基金资助:
    北京科技创新服务能力建设项目(PXM2016_014223_000025);广东省科技重大专项项目(190826175545233)

Implementation of Financial Venture Capital Score Card Model Based on Logistic Regression

BIAN Yu-ning, LU Li-kun, LI Ye-li, ZENG Qing-tao, SUN Yan-xiong   

  1. Beijing Institute of Graphic Communication,Beijing 102600,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:BIAN Yu-ning,born in 1994,master.Her main research interests include data mining and so on.
  • Supported by:
    This work was supported by the Beijing Science and Technology Innovation Service Capacity Building Project(PXM2016_014223_000025) and Major Special Projects of Science and Technology in Guangdong Province(190826175545233).

摘要: 文以当前银行信贷业务中客户违约问题为出发点,将客户违约率和信贷评分卡分值的关系合理映射。运用逻辑回归建立评分卡预测模型,使用梯度下降算法来实现银行风险投资中客户评分卡的构建。首先加载数据并对数据进行分析,接着划分数据集,并使用跨时间验证集作为模型最后的验证。最后使用KS值和AOC曲线双向评价模型的稳定性。实验证明,采用所提方法构建的评分卡模型具有较好的稳定性。

关键词: 机器学习, 金融风投, 逻辑回归, 评分卡

Abstract: This paper takes the problem of customer default in the current bank credit business as the starting point and maps the relationship between customer default rate and credit score card value reasonably.The logistic regression is used to build the prediction model of the score card and the gradient descent algorithm is used to construct the customer score card in the bank venture capital.The data is first loaded and analyzed,then the data set is partitioned and the cross-time validation set is used as the final validation of the model.Finally,KS value and AOC curve are used to evaluate the stability of the model.Experimental results show that the score card model constructed by the proposed method has good stability.

Key words: Financial venture capital, Logistic regression, Machine learning, Score card

中图分类号: 

  • TP391
[1] YI S.Research on Internet credit risk early warning based on big data analysis [D].Xianyang:Northwest A & F University,2019.
[2] ZHOU H J.Research on risk management of credit card busi-ness of commercial banks [D].Shanghai:Shanghai JiaotongUniversity,2012.
[3] WANG Z C,XIAO Z J,YAN Z G.Research on Optimization of logistic regression classification identification [J].Journal of Qilu University of technology,2019(5):47-51.
[4] LUO L G,PEI X J,HUANG R Q,et al.Landslide susceptibility by GIS based on certainty factor and logistic regression model in Jiuzhaigou scenic area[J/OL]. Journal of Engineering Geology,[2020-03-23].https://doi.org/10.13544/j.cnki.jeg.2019-202.
[5] BAI J Y.Financial risk identification model based on classicscore card and machine learning and its application [D].Tianjin:Tianjin University of Commerce,2019.
[6] TANG H.Campus Personalized Learning Resource Recommendation System Based on logistic regression model [J].Electronic Technology and Software Engineering,2019(22):164-165.
[7] DU S M.Prediction of e-commerce users' repurchase behavior based on classification model [D].Hangzhou:Hangzhou Normal University,2019.
[8] PAPAKOSTA M A.Geographical variation in morphometry,craniometry,and diet of amammalian species (Stone marten,Martes foina) using data mining[J].Turkish Journal of Zoology,2018,42:99-106.
[9] THOMAS L.A Survey of Credit and Behavioural Scoring:Forecasting financial risk of lending to consumers[J].International Journal of Forecasting,2000,16(2):149-172.
[10] FAWCETT T.An introduction to ROC analysis[J].PatternRecognition Letters,2006,27(8):861-874.
[11] GU G,HE Y.Research on the application of data mining to customer relationship management in the mobile communication industry[C]//IEEE International Conference on Computer Science and Information Technology.IEEE,2010:597-599.
[12] SOUZA J.Data Mining and Machine Learning to Promote Smart Cities:A Systematic Review from 2000 to 2018[J].Sustainability,2019,11(4):1077.
[13] PAPAKOSTA M A.Geographical variation in morphometry,craniometry,and diet of amammalian species (Stone marten,Martes foina) using data mining[J].Turkish Journal Ournal of Zoology,2018,42:99-106.
[14] LEE E,LEE B.Herding Behavior in Online P2P Lending:An Empirical Investigation[J].Electronic Commerce Research and Application,2012,11(5):495-503.
[1] 冷典典, 杜鹏, 陈建廷, 向阳.
面向自动化集装箱码头的AGV行驶时间估计
Automated Container Terminal Oriented Travel Time Estimation of AGV
计算机科学, 2022, 49(9): 208-214. https://doi.org/10.11896/jsjkx.210700028
[2] 宁晗阳, 马苗, 杨波, 刘士昌.
密码学智能化研究进展与分析
Research Progress and Analysis on Intelligent Cryptology
计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053
[3] 李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩.
基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究
Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network
计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094
[4] 张光华, 高天娇, 陈振国, 于乃文.
基于N-Gram静态分析技术的恶意软件分类研究
Study on Malware Classification Based on N-Gram Static Analysis Technology
计算机科学, 2022, 49(8): 336-343. https://doi.org/10.11896/jsjkx.210900203
[5] 何强, 尹震宇, 黄敏, 王兴伟, 王源田, 崔硕, 赵勇.
基于大数据的进化网络影响力分析研究综述
Survey of Influence Analysis of Evolutionary Network Based on Big Data
计算机科学, 2022, 49(8): 1-11. https://doi.org/10.11896/jsjkx.210700240
[6] 陈明鑫, 张钧波, 李天瑞.
联邦学习攻防研究综述
Survey on Attacks and Defenses in Federated Learning
计算机科学, 2022, 49(7): 310-323. https://doi.org/10.11896/jsjkx.211000079
[7] 肖治鸿, 韩晔彤, 邹永攀.
基于多源数据和逻辑推理的行为识别技术研究
Study on Activity Recognition Based on Multi-source Data and Logical Reasoning
计算机科学, 2022, 49(6A): 397-406. https://doi.org/10.11896/jsjkx.210300270
[8] 姚烨, 朱怡安, 钱亮, 贾耀, 张黎翔, 刘瑞亮.
一种基于异质模型融合的 Android 终端恶意软件检测方法
Android Malware Detection Method Based on Heterogeneous Model Fusion
计算机科学, 2022, 49(6A): 508-515. https://doi.org/10.11896/jsjkx.210700103
[9] 李亚茹, 张宇来, 王佳晨.
面向超参数估计的贝叶斯优化方法综述
Survey on Bayesian Optimization Methods for Hyper-parameter Tuning
计算机科学, 2022, 49(6A): 86-92. https://doi.org/10.11896/jsjkx.210300208
[10] 赵璐, 袁立明, 郝琨.
多示例学习算法综述
Review of Multi-instance Learning Algorithms
计算机科学, 2022, 49(6A): 93-99. https://doi.org/10.11896/jsjkx.210500047
[11] 王飞, 黄涛, 杨晔.
基于Stacking多模型融合的IGBT器件寿命的机器学习预测算法研究
Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion
计算机科学, 2022, 49(6A): 784-789. https://doi.org/10.11896/jsjkx.210400030
[12] 许杰, 祝玉坤, 邢春晓.
机器学习在金融资产定价中的应用研究综述
Application of Machine Learning in Financial Asset Pricing:A Review
计算机科学, 2022, 49(6): 276-286. https://doi.org/10.11896/jsjkx.210900127
[13] 李野, 陈松灿.
基于物理信息的神经网络:最新进展与展望
Physics-informed Neural Networks:Recent Advances and Prospects
计算机科学, 2022, 49(4): 254-262. https://doi.org/10.11896/jsjkx.210500158
[14] 么晓明, 丁世昌, 赵涛, 黄宏, 罗家德, 傅晓明.
大数据驱动的社会经济地位分析研究综述
Big Data-driven Based Socioeconomic Status Analysis:A Survey
计算机科学, 2022, 49(4): 80-87. https://doi.org/10.11896/jsjkx.211100014
[15] 章晓庆, 方建生, 肖尊杰, 陈浜, RisaHIGASHITA, 陈婉, 袁进, 刘江.
基于眼前节相干光断层扫描成像的核性白内障分类算法
Classification Algorithm of Nuclear Cataract Based on Anterior Segment Coherence Tomography Image
计算机科学, 2022, 49(3): 204-210. https://doi.org/10.11896/jsjkx.201100085
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!