An Intelligent Classification Method in Bank Customer Relationship Management

Zhang, J; Lu, J; Zhang, G

An Intelligent Classification Method in Bank Customer Relationship Management

Zhang, J Lu, J

Zhang, G

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Publisher:: World Scientific Publishing
Publication Type:: Journal Article
Citation:: New Mathematics and Natural Computation, 2007, 3 (1), pp. 111 - 121
Issue Date:: 2007-01

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Field	Value	Language
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2007-01	en_US
dc.identifier.citation	New Mathematics and Natural Computation, 2007, 3 (1), pp. 111 - 121	en_US
dc.identifier.issn	1793-0057	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3477
dc.description.abstract	Customer classification is one of the major tasks in customer relationship management. Customers often have both static characteristics and dynamic behavioral features. Using both kinds of data to conduct comprehensive analysis can enhance the reasonability of customer classification. In the proposed classification method, customer dynamic data is clustered using a hybrid genetic algorithm. The result is then combined with customer static data to give reasonable customer segmentation supported by neural network technique. A bank dataset-based experiment shows that applying the proposed method can obviously improve the accuracy of customer classification comparing with the traditional methods where only static data is used.	en_US
dc.publisher	World Scientific Publishing	en_US
dc.relation.ispartof	New Mathematics and Natural Computation	en_US
dc.title	An Intelligent Classification Method in Bank Customer Relationship Management	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	3	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	1	en_US
pubs.volume	3	en_US

Abstract:

Customer classification is one of the major tasks in customer relationship management. Customers often have both static characteristics and dynamic behavioral features. Using both kinds of data to conduct comprehensive analysis can enhance the reasonability of customer classification. In the proposed classification method, customer dynamic data is clustered using a hybrid genetic algorithm. The result is then combined with customer static data to give reasonable customer segmentation supported by neural network technique. A bank dataset-based experiment shows that applying the proposed method can obviously improve the accuracy of customer classification comparing with the traditional methods where only static data is used.

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http://hdl.handle.net/10453/3477