Social security and social welfare data mining: An overview

Cao, L

Social security and social welfare data mining: An overview

Cao, L

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2012, 42 (6), pp. 837 - 853
Issue Date:: 2012-01-16

Closed Access

	Filename	Description	Size
	2012001191OK.pdf	Published Version	743.37 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2012-01-16	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2012, 42 (6), pp. 837 - 853	en_US
dc.identifier.issn	1094-6977	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31611
dc.description.abstract	The importance of social security and social welfare business has been increasingly recognized in more and more countries. It impinges on a large proportion of the population and affects government service policies and peoples life quality. Typical welfare countries, such as Australia and Canada, have accumulated a huge amount of social security and social welfare data. Emerging business issues such as fraudulent outlays, and customer service and performance improvements challenge existing policies, as well as techniques and systems including data matching and business intelligence reporting systems. The need for a deep understanding of customers and customer-government interactions through advanced data analytics has been increasingly recognized by the community at large. So far, however, no substantial work on the mining of social security and social welfare data has been reported. For the first time in data mining and machine learning, and to the best of our knowledge, this paper draws a comprehensive overall picture and summarizes the corresponding techniques and illustrations to analyze social security/welfare data, namely, social security data mining (SSDM), based on a thorough review of a large number of related references from the past half century. In particular, we introduce an SSDM framework, including business and research issues, social security/welfare services and data, as well as challenges, goals, and tasks in mining social security/welfare data. A summary of SSDM case studies is also presented with substantial citations that direct readers to more specific techniques and practices about SSDM. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews	en_US
dc.relation.isbasedon	10.1109/TSMCC.2011.2177258	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Social security and social welfare data mining: An overview	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	42	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	42	en_US

Abstract:

The importance of social security and social welfare business has been increasingly recognized in more and more countries. It impinges on a large proportion of the population and affects government service policies and peoples life quality. Typical welfare countries, such as Australia and Canada, have accumulated a huge amount of social security and social welfare data. Emerging business issues such as fraudulent outlays, and customer service and performance improvements challenge existing policies, as well as techniques and systems including data matching and business intelligence reporting systems. The need for a deep understanding of customers and customer-government interactions through advanced data analytics has been increasingly recognized by the community at large. So far, however, no substantial work on the mining of social security and social welfare data has been reported. For the first time in data mining and machine learning, and to the best of our knowledge, this paper draws a comprehensive overall picture and summarizes the corresponding techniques and illustrations to analyze social security/welfare data, namely, social security data mining (SSDM), based on a thorough review of a large number of related references from the past half century. In particular, we introduce an SSDM framework, including business and research issues, social security/welfare services and data, as well as challenges, goals, and tasks in mining social security/welfare data. A summary of SSDM case studies is also presented with substantial citations that direct readers to more specific techniques and practices about SSDM. © 2012 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/31611