| 黄志刚,刘志惠,朱建林.多源数据信用评级普适模型栈框架的构建与应用[J].数量经济技术经济研究,2019,(4):155-168 |
| 多源数据信用评级普适模型栈框架的构建与应用 |
| A General Stack Framework of Credit Risk Rating Models Based on Multi Source Data |
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| DOI： |
| 中文关键词: 风控 信用评分 评分模型 机器学习 模型栈框架 |
| 英文关键词: Risk Management Credit Score Score Card Model Machine Learning Stack Framework Model |
| 基金项目:本文获得国家自然科学基金项目（71473039）、国家社会科学基金重大专项（18VDL012）的资助。 |
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| 中文摘要: |
| 研究目标：研究多源数据普适模型栈在信用评级中的构建与应用。研究方法：从我国在线信贷行业实际情况出发，提出一种基于多源数据的普适模型栈评分框架，该框架可以根据各个申请人不同的数据基础，自由选择纳入评分模型数据，生成子评分模型，然后再将子评分模型转换为常见的信用评分卡模型。研究发现：基于多源数据的普适模型栈评分框架不但灵活、普适，其评分有效性也比单个XGBoost信用评分模型更好。研究创新：将机器学习模型与传统评分卡模型进行了完美的融合。研究价值：解决了机器学习模型在信用风险管理中可解释性差的问题。 |
| 英文摘要: |
| Research Objectives:General stack scoring framework based on the data from a variety of channels. Research Methods: Based on the actual situation of the online lending industry in China, this paper proposes a general stack scoring framework based on the data from a variety of channels. This framework can freely select the data of the score model according to the different data base of each lender,and then the sub-scoring model is transformed into a common credit scoring card model. Research Findings: The general stack scoring framework is not only flexible and general, but also has better effectiveness than a single XGBoost credit scoring model.Research Innovations: The machine learning model and the traditional scorecard model are perfectly integrated.Research Value: The problem of poor interpretability of machine learning in credit risk management is solved. |
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