Customer segmentation in private banking sector using machine learning techniques

Ion Smeureanu, Gheorghe Ruxanda and Laura Maria Badea

Journal of Business Economics and Management, 2013, vol. 14, issue 5, 923-939

Abstract: Machine learning techniques have proven good performance in classification matters of all kinds: medical diagnosis, character recognition, credit default and fraud prediction, and also foreign exchange market prognosis. Customer segmentation in private banking sector is an important step for profitable business development, enabling financial institutions to address their products and services to homogeneous classes of customers. This paper approaches two of the most popular machine learning techniques, Neural Networks and Support Vector Machines, and describes how each of these perform in a segmentation process.

Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.3846/16111699.2012.749807 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jbemgt:v:14:y:2013:i:5:p:923-939

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TBEM20

DOI: 10.3846/16111699.2012.749807

Access Statistics for this article

Journal of Business Economics and Management is currently edited by Izolda Joksiene, Romualdas Ginevicius and Ieva Meidute

More articles in Journal of Business Economics and Management from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().