Discovering Debtor Patterns of Centrelink Customers

Zhao, Y; Cao, L; Morrow, Y; Ou, Y; Ni, J; Zhang, C

Discovering Debtor Patterns of Centrelink Customers

Zhao, Y Cao, L

Morrow, Y Ou, Y Ni, J Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Conferences in Research and Practice in Information Technology Series, 2006, 61 pp. 135 - 144
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Morrow, Y	en_US
dc.contributor.author	Ou, Y	en_US
dc.contributor.author	Ni, J	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	Conferences in Research and Practice in Information Technology Series, 2006, 61 pp. 135 - 144	en_US
dc.identifier.issn	1445-1336	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2550
dc.description.abstract	Data mining is currently becoming an increasingly hot research field, but a large gap still remains between the research of data mining and its application in real-world business. As one of the largest data users in Australia, Centrelink has huge volume of data in data warehouse and tapes. Based on the available data, Centrelink is seeking to find underlying patterns to be able to intervene earlier to prevent or minimize debt. To discover the debtor patterns of Centrelink customers and bridge the gap between data mining research and application, we have done a project on improving income reporting to discover the patterns of those customers who were or are in debt to Centrelink. Two data models were built respectively for demographic data and activity data, and decision tree and sequence mining were used respectively to discover demographic patterns and activity sequence patterns of debtors. The project produced some potentially interesting results, and paved the way for more data mining applications in Centrelink in near future. © 2006, Australian Computer Society, Inc.	en_US
dc.relation.ispartof	Conferences in Research and Practice in Information Technology Series	en_US
dc.title	Discovering Debtor Patterns of Centrelink Customers	en_US
dc.type	Conference Proceeding
utslib.citation.volume	61	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, Australia	en_US
dc.location.activity	Las Vegas USA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
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
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	61	en_US

Abstract:

Data mining is currently becoming an increasingly hot research field, but a large gap still remains between the research of data mining and its application in real-world business. As one of the largest data users in Australia, Centrelink has huge volume of data in data warehouse and tapes. Based on the available data, Centrelink is seeking to find underlying patterns to be able to intervene earlier to prevent or minimize debt. To discover the debtor patterns of Centrelink customers and bridge the gap between data mining research and application, we have done a project on improving income reporting to discover the patterns of those customers who were or are in debt to Centrelink. Two data models were built respectively for demographic data and activity data, and decision tree and sequence mining were used respectively to discover demographic patterns and activity sequence patterns of debtors. The project produced some potentially interesting results, and paved the way for more data mining applications in Centrelink in near future. © 2006, Australian Computer Society, Inc.

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

http://hdl.handle.net/10453/2550