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

doi:10.11959/j.issn.2096-0271.2016044

您当前的位置：

首页 >

文章列表页 >

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

APPLICATION | 更新时间：2024-06-03

- Real-time data-mining-based anomaly inquiry monitoring model of personal credit reference system and it's application
- Big Data Research Vol. 2, Issue 4, Pages: 2016044(2016)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2016044
  CLC： TP399
- Online First：2016-07，
  
  Published：20 July 2016
- 稿件说明：
移动端阅览
Qian YAO, Huamei XIE, Zhigang JING, et al. Real-time data-mining-based anomaly inquiry monitoring model of personal credit reference system and it's application[J]. Big Data Research2016, 2(4): 2016044.
DOI：

Qian YAO, Huamei XIE, Zhigang JING, et al. Real-time data-mining-based anomaly inquiry monitoring model of personal credit reference system and it's application[J]. Big Data Research2016, 2(4): 2016044. DOI： 10.11959/j.issn.2096-0271.2016044.

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。

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.

关键词

Keywords

references

HAN J , KAMBER M . Data mining concepts and techniques [M ] . Translated by FAN M,MENG X F . Beijing : China Machine PressPress , 2012 .

PANG-NING T , STEINBACH M , KUMAR V . Introduction to data mining [M ] . Translated by FAN M,FAN H J . Beijing : Posts ＆ Telecom PressPress , 2011 .

中华人民共和国国务院 . Introduction to data mining [M ] . 北京 : 中国法制出版社 , 2013 .

中华人民共和国国务院 .. The State Council of the People’s Republic of China [M ] . Beijing : China Legal Publishing HousePress , 2013 .

汪路 . 论征信的本质及其主要特征 [J ] . 西部金融 , 2010 ( 6 ): 60 - 62 .

WANG L . On the essence and main features of credit reference [J ] . West China Finance , 2010 ( 6 ): 60 - 62 .

Views

726

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Discovery of irradiation mechanism based on big data of material simulation

Analysis and utilization framework of electronic medical records under the background of smart hospital construction

Research on demand identification for customized bus based on multi-source mobility data

Big data analysis and application technology innovation platform

Credit scoring:basic theory and applications

Related Author

Changjun HU

Genshen CHU

Dandan CHEN

Shuai REN

He BAI

Yuanjie HE

Huizhao LI

Chonghui GUO

Related Institution

Engineering Research Center of Intelligent Supercomputing, Ministry of Education

School of Computer and Communication Engineering, University of Science and Technology Beijing

Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University

School of Transportation and Logistics,Southwest Jiaotong University

Department of Civil and Environmental Engineering,University of Washington

Postal code：100079
Tel：（010）53879208、53859533 Email：bdr@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：273427 Visits Today：333

⁰