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Domain knowledge based segmentation of online banking customers

Domain knowledge based segmentation of online banking customers
Domain knowledge based segmentation of online banking customers
The share of the services offered via the Internet by nowadays banking companies is quickly growing, making of the understanding of online customers one of the major concerns. Data mining tools have proven their efficiency in addressing this challenge by providing unsupervised quantitative techniques to identify those segments of customers with similar characteristics. This paper will focus on segmenting an online banking customer base in a meaningful way for the business by enhancing an unsupervised quantitative technique approach with domain knowledge. Both traditional and knowledge-based approaches will be applied and evaluated. Thanks to an extensive description and discussion of the new insights, the complementarity of the two approaches is illustrated.
1088-467X
S163-S184
Seret, Alex
34884644-0660-4bce-9a0d-97c99492b892
Bejinaru, Andreea
3d5a6f83-8613-464e-bc36-d790cf86e622
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Davison, Matt
d3bae0fe-6789-4a28-a3a6-50005992c8e4
Jofré, Alejandro
e76911eb-122d-403b-9329-27ae13b662b1
Maldonado, Sebastián
9e5fb121-d905-4337-beb3-bba6f7da9ae2
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
Bravo, Cristián
b22c4145-644e-40ee-85d8-431c59c3c71b
Seret, Alex
34884644-0660-4bce-9a0d-97c99492b892
Bejinaru, Andreea
3d5a6f83-8613-464e-bc36-d790cf86e622
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Bravo, Cristián
b22c4145-644e-40ee-85d8-431c59c3c71b
Davison, Matt
d3bae0fe-6789-4a28-a3a6-50005992c8e4
Jofré, Alejandro
e76911eb-122d-403b-9329-27ae13b662b1
Maldonado, Sebastián
9e5fb121-d905-4337-beb3-bba6f7da9ae2
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b

Seret, Alex, Bejinaru, Andreea, Baesens, Bart, Davison, Matt, Jofré, Alejandro, Maldonado, Sebastián and Weber, Richard , Bravo, Cristián (ed.) (2015) Domain knowledge based segmentation of online banking customers. Intelligent Data Analysis, 19 (s1), S163-S184. (doi:10.3233/IDA-150776).

Record type: Article

Abstract

The share of the services offered via the Internet by nowadays banking companies is quickly growing, making of the understanding of online customers one of the major concerns. Data mining tools have proven their efficiency in addressing this challenge by providing unsupervised quantitative techniques to identify those segments of customers with similar characteristics. This paper will focus on segmenting an online banking customer base in a meaningful way for the business by enhancing an unsupervised quantitative technique approach with domain knowledge. Both traditional and knowledge-based approaches will be applied and evaluated. Thanks to an extensive description and discussion of the new insights, the complementarity of the two approaches is illustrated.

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More information

Published date: 9 October 2015

Identifiers

Local EPrints ID: 425731
URI: http://eprints.soton.ac.uk/id/eprint/425731
ISSN: 1088-467X
PURE UUID: 120a9632-795e-4ca5-bfbe-f2f5db503cd6
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668
ORCID for Cristián Bravo: ORCID iD orcid.org/0000-0003-1579-1565

Catalogue record

Date deposited: 02 Nov 2018 17:30
Last modified: 27 Apr 2022 01:52

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Contributors

Author: Alex Seret
Author: Andreea Bejinaru
Author: Bart Baesens ORCID iD
Editor: Cristián Bravo ORCID iD
Author: Matt Davison
Author: Alejandro Jofré
Author: Sebastián Maldonado
Author: Richard Weber

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