大数据 ›› 2016, Vol. 2 ›› Issue (4): 83-92.doi: 10.11959/j.issn.2096-0271.2016044

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基于数据挖掘的个人征信系统异常查询实时监测模型及其应用

姚前,谢华美,景志刚,胡青青,司恩哲   

  1. 中国人民银行征信中心,北京 100031
  • 出版日期:2016-07-20 发布日期:2017-04-27

Real-time data-mining-based anomaly inquiry monitoring model of personal credit reference system and it's application

Qian YAO,Huamei XIE,Zhigang JING,Qingqing HU,Enzhe SI   

  1. Credit Reference Center,the People’s Bank of China,Beijing 100031,China
  • Online:2016-07-20 Published:2017-04-27

摘要:

选择个人征信系统最新36个月9亿条查询记录,根据用户查询行为的不同波动特征进行了模型细分,探讨了4种异常查询实时监测模型。结果表明,基于数据挖掘的个人征信系统异常查询实时监测模型应用于个人查询量预测是可行的,且效果良好。该模型的成功上线和不断修正,将对个人征信系统的违规查询行为产生威慑作用,倒逼查询机构加强内部管理,合法使用信用信息,以保障信息主体的权益,促进征信市场健康发展。

关键词: 数据挖掘, 个人征信系统, 异常查询, 违规查询, 实时监测

Abstract:

The data selected contained 900 million query records in the latest 36 months from the personal credit reference system database.The model was subdivided according to different volatility characteristics of each user’s query behavior,and four types of real-time anomaly inquiry monitoring models were discussed and developed.Results indicate that the anomaly inquiry monitoring model is feasible to apply on predicting anomaly query behaviors and showed positive effects.The successful application and constant perfection of the model would definitely exert deterrent effect on illegal query behaviors,force commercial banks to strengthen internal management,protect individual’s private information and right,and promote the healthy development of the credit reference market.

Key words: data mining, personal credit reference system, anomaly inquiry, illegal inquiry, real-time monitoring

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