Less effort, more outcomes: Optimising debt recovery with decision trees

Zhao, Y; Bohlscheid, H; Wu, S; Cao, L

Less effort, more outcomes: Optimising debt recovery with decision trees

Zhao, Y Bohlscheid, H Wu, S Cao, L

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2010, pp. 655 - 660
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Bohlscheid, H	en_US
dc.contributor.author	Wu, S	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2010, pp. 655 - 660	en_US
dc.identifier.isbn	9780769542577	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16223
dc.description.abstract	This paper presents a real-world application of data mining techniques to optimise debt recovery in social security. The traditional method of contacting a customer for the purpose of putting in place a debt recovery schedule has been an out-bound phone call, and by and large, customers are chosen at random. This obsolete and inefficient method of selecting customers for debt recovery purposes has existed for years and in order to improve this process, decision trees were built to model debt recovery and predict the response of customers if contacted by phone. Test results on historical data show that, the built model is effective to rank customers in their likelihood of entering into a successful debt recovery repayment schedule. If contacting the top 20 per cent of customers in debt, instead of contacting all of them, approximately 50 per cent of repayments would be received. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDMW.2010.114	en_US
dc.title	Less effort, more outcomes: Optimising debt recovery with decision trees	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Sydney, NSW, Australia	en_US
dc.location.activity	ISI:000270437100012
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.publication-status	Published	en_US

Abstract:

This paper presents a real-world application of data mining techniques to optimise debt recovery in social security. The traditional method of contacting a customer for the purpose of putting in place a debt recovery schedule has been an out-bound phone call, and by and large, customers are chosen at random. This obsolete and inefficient method of selecting customers for debt recovery purposes has existed for years and in order to improve this process, decision trees were built to model debt recovery and predict the response of customers if contacted by phone. Test results on historical data show that, the built model is effective to rank customers in their likelihood of entering into a successful debt recovery repayment schedule. If contacting the top 20 per cent of customers in debt, instead of contacting all of them, approximately 50 per cent of repayments would be received. © 2010 IEEE.

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http://hdl.handle.net/10453/16223