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  4. 基于大数据的信贷违约风险影响因素研究
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基于大数据的信贷违约风险影响因素研究

Full metadata

Title
基于大数据的信贷违约风险影响因素研究
Description

随着信息通信技术在金融科技领域中得到广泛应用,传统金融机构依靠互联网技术极大的提升了自己的金融服务能力和金融服务效率。但与此同时,作为一个新兴业态,与互联网金融服务配套的法律制度和保障措施还未完善,特别是互联网借贷业务,贷前的风控系统不完善,同时还缺乏贷后管理机制,造成了网络贷款平台不断出现爆雷现象。仅2018年7月一个月内就有200多家平台出现问题,而到2020年底为止,出现问题的在线借贷平台高达80%。为了更好的保障在线借贷平台和互联网金融行业的健康发展,亟需完善个人征信体系建设,科学评估借款人违约风险。为了解决这一问题,本文首先对现有研究进行了理论梳理,找到可能对违约风险产生影响的因素,并总结为个人特征、社交网络特征、金融特征等三方面的因素。在这之后,从社交网络特征对违约风险进行了深入分析。其次,利用大数据分析方法,构建了随机森林信用评价模型。最后,文章还通过与不同数据集上的相同模型、相同数据集上的不同模型进行对比,对本文构建模型的有效性进行了评估。
研究结论表明:(1)用户的社交网络特征对用户违约风险、欺诈等级具有一定的解释力度,其中用户通话类社交特征对用户欺诈等级的识别效果最好,其次为风险等级,违约标签的识别效果最差,而且用户的地域特征对社交网络特征有显著的调节作用。(2)通过随机森林模型,本文发现年龄、贷款金额是影响客户违约风险和欺诈等级的最重要的因素。(3)比较多元回归模型和随机森林模型,随机森林模型对样本用户特征重要性探索的准确度要高于多元回归模型。
根据上述结论,本文提出了相应了建议:(1)在线借贷平台在判断用户违约风险时,应该在现有的分析框架中考虑用户社交特征来提升用户风险预测精度;(2)信贷公司应该将随机森林等方法纳入到用户是否违约、风险等级和欺诈等级的预测中,这样会显著的提升公司对用户违约、欺诈等级的预测精度。

Date Created
2022
Contributors
  • Han, Wei (Author)
  • Shen, Wei (Thesis advisor)
  • Hu, Jie (Thesis advisor)
  • Zheng, Zhiqiang (Committee member)
  • Arizona State University (Publisher)
Topical Subject
  • Business Administration
  • 信用评估
  • 多元回归分析
  • 违约风险
  • 随机森林模型
Resource Type
Text
Genre
Doctoral Dissertation
Academic theses
Extent
105 pages
Language
chi
Copyright Statement
In Copyright
Reuse Permissions
All Rights Reserved
Primary Member of
ASU Electronic Theses and Dissertations
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.168542
Level of coding
minimal
Cataloging Standards
asu1
System Created
  • 2022-08-22 04:34:45
System Modified
  • 2022-08-22 04:35:11
  •     
  • 1 year 1 month ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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