Loading…
An empirical study on risk data quality management
Risk management is a critical core process of a financial corporation, which has been attracted more attention by the governors since The Financial Crisis. New regulations require more strict control over corporation's risk, which needs data of better quality. In this paper, we propose a compre...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Request full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 514 |
| container_issue | |
| container_start_page | 511 |
| container_title | |
| container_volume | 1 |
| creator | Lizheng Jiang Jiantao Zhao |
| description | Risk management is a critical core process of a financial corporation, which has been attracted more attention by the governors since The Financial Crisis. New regulations require more strict control over corporation's risk, which needs data of better quality. In this paper, we propose a comprehensive framework for risk data quality management. New data quality dimensions that combine business rules are designed to reflect the risk management requirements. According to the detailed dimensions, we examine large amount of heterogeneous data in a bank to find the difference between the normal standard and the current data. Enterprises should take appropriate measures to improve the data quality to meet the standard. Experiments show that the framework is effective. |
| doi_str_mv | 10.1109/ICIII.2012.6339714 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_6339714</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6339714</ieee_id><sourcerecordid>6339714</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-fbf3247ec5dd1a5529c529180c97deba3d075a9cbb9ebd6632dda69eb01fb7fc3</originalsourceid><addsrcrecordid>eNo9j81Kw0AUhUerYFv7ArqZF0icOzcz01mW4E-g4Kb7cucnZTSJNUkXeXsLVheH78AHBw5jDyByAGGfqrKqqlwKkLlGtAaKK7ayZg2FNggWwVyzuQSlMiiMnLHFn5Dq5l8ofccWw_AhhEaNYs7kpuOxPaY-eWr4MJ7CxL863qfhkwcaiX-fqEnjxFvq6BDb2I337LamZoirC5ds9_K8K9-y7ftrVW62WbJizGpXoyxM9CoEIKWk9efAWnhrQnSEQRhF1jtnowtaowyB9LkLqJ2pPS7Z4-9sijHuj31qqZ_2l-v4A68rSX4</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>An empirical study on risk data quality management</title><source>IEEE Xplore All Conference Series</source><creator>Lizheng Jiang ; Jiantao Zhao</creator><creatorcontrib>Lizheng Jiang ; Jiantao Zhao</creatorcontrib><description>Risk management is a critical core process of a financial corporation, which has been attracted more attention by the governors since The Financial Crisis. New regulations require more strict control over corporation's risk, which needs data of better quality. In this paper, we propose a comprehensive framework for risk data quality management. New data quality dimensions that combine business rules are designed to reflect the risk management requirements. According to the detailed dimensions, we examine large amount of heterogeneous data in a bank to find the difference between the normal standard and the current data. Enterprises should take appropriate measures to improve the data quality to meet the standard. Experiments show that the framework is effective.</description><identifier>ISSN: 2155-1456</identifier><identifier>ISBN: 1467319325</identifier><identifier>ISBN: 9781467319324</identifier><identifier>EISSN: 2155-1472</identifier><identifier>EISBN: 9781467319317</identifier><identifier>EISBN: 1467319317</identifier><identifier>DOI: 10.1109/ICIII.2012.6339714</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; business rule ; data quality ; Phase measurement ; Process control ; quality dimensions ; Quality management ; Risk management</subject><ispartof>2012 International Conference on Information Management, Innovation Management and Industrial Engineering, 2012, Vol.1, p.511-514</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6339714$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54553,54918,54930</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6339714$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lizheng Jiang</creatorcontrib><creatorcontrib>Jiantao Zhao</creatorcontrib><title>An empirical study on risk data quality management</title><title>2012 International Conference on Information Management, Innovation Management and Industrial Engineering</title><addtitle>ICIII</addtitle><description>Risk management is a critical core process of a financial corporation, which has been attracted more attention by the governors since The Financial Crisis. New regulations require more strict control over corporation's risk, which needs data of better quality. In this paper, we propose a comprehensive framework for risk data quality management. New data quality dimensions that combine business rules are designed to reflect the risk management requirements. According to the detailed dimensions, we examine large amount of heterogeneous data in a bank to find the difference between the normal standard and the current data. Enterprises should take appropriate measures to improve the data quality to meet the standard. Experiments show that the framework is effective.</description><subject>Accuracy</subject><subject>business rule</subject><subject>data quality</subject><subject>Phase measurement</subject><subject>Process control</subject><subject>quality dimensions</subject><subject>Quality management</subject><subject>Risk management</subject><issn>2155-1456</issn><issn>2155-1472</issn><isbn>1467319325</isbn><isbn>9781467319324</isbn><isbn>9781467319317</isbn><isbn>1467319317</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9j81Kw0AUhUerYFv7ArqZF0icOzcz01mW4E-g4Kb7cucnZTSJNUkXeXsLVheH78AHBw5jDyByAGGfqrKqqlwKkLlGtAaKK7ayZg2FNggWwVyzuQSlMiiMnLHFn5Dq5l8ofccWw_AhhEaNYs7kpuOxPaY-eWr4MJ7CxL863qfhkwcaiX-fqEnjxFvq6BDb2I337LamZoirC5ds9_K8K9-y7ftrVW62WbJizGpXoyxM9CoEIKWk9efAWnhrQnSEQRhF1jtnowtaowyB9LkLqJ2pPS7Z4-9sijHuj31qqZ_2l-v4A68rSX4</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Lizheng Jiang</creator><creator>Jiantao Zhao</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201210</creationdate><title>An empirical study on risk data quality management</title><author>Lizheng Jiang ; Jiantao Zhao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-fbf3247ec5dd1a5529c529180c97deba3d075a9cbb9ebd6632dda69eb01fb7fc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>business rule</topic><topic>data quality</topic><topic>Phase measurement</topic><topic>Process control</topic><topic>quality dimensions</topic><topic>Quality management</topic><topic>Risk management</topic><toplevel>online_resources</toplevel><creatorcontrib>Lizheng Jiang</creatorcontrib><creatorcontrib>Jiantao Zhao</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lizheng Jiang</au><au>Jiantao Zhao</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An empirical study on risk data quality management</atitle><btitle>2012 International Conference on Information Management, Innovation Management and Industrial Engineering</btitle><stitle>ICIII</stitle><date>2012-10</date><risdate>2012</risdate><volume>1</volume><spage>511</spage><epage>514</epage><pages>511-514</pages><issn>2155-1456</issn><eissn>2155-1472</eissn><isbn>1467319325</isbn><isbn>9781467319324</isbn><eisbn>9781467319317</eisbn><eisbn>1467319317</eisbn><abstract>Risk management is a critical core process of a financial corporation, which has been attracted more attention by the governors since The Financial Crisis. New regulations require more strict control over corporation's risk, which needs data of better quality. In this paper, we propose a comprehensive framework for risk data quality management. New data quality dimensions that combine business rules are designed to reflect the risk management requirements. According to the detailed dimensions, we examine large amount of heterogeneous data in a bank to find the difference between the normal standard and the current data. Enterprises should take appropriate measures to improve the data quality to meet the standard. Experiments show that the framework is effective.</abstract><pub>IEEE</pub><doi>10.1109/ICIII.2012.6339714</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2155-1456 |
| ispartof | 2012 International Conference on Information Management, Innovation Management and Industrial Engineering, 2012, Vol.1, p.511-514 |
| issn | 2155-1456 2155-1472 |
| language | eng |
| recordid | cdi_ieee_primary_6339714 |
| source | IEEE Xplore All Conference Series |
| subjects | Accuracy business rule data quality Phase measurement Process control quality dimensions Quality management Risk management |
| title | An empirical study on risk data quality management |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T15%3A41%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=An%20empirical%20study%20on%20risk%20data%20quality%20management&rft.btitle=2012%20International%20Conference%20on%20Information%20Management,%20Innovation%20Management%20and%20Industrial%20Engineering&rft.au=Lizheng%20Jiang&rft.date=2012-10&rft.volume=1&rft.spage=511&rft.epage=514&rft.pages=511-514&rft.issn=2155-1456&rft.eissn=2155-1472&rft.isbn=1467319325&rft.isbn_list=9781467319324&rft_id=info:doi/10.1109/ICIII.2012.6339714&rft.eisbn=9781467319317&rft.eisbn_list=1467319317&rft_dat=%3Cieee_CHZPO%3E6339714%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i90t-fbf3247ec5dd1a5529c529180c97deba3d075a9cbb9ebd6632dda69eb01fb7fc3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6339714&rfr_iscdi=true |