Profiling internet banking users: A knowledge discovery in data mining process model based approach

Gunjan Mansingh (), Lila Rao (), Kweku-Muata Osei-Bryson () and Annette Mills ()
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Gunjan Mansingh: The University of the West Indies
Lila Rao: The University of the West Indies
Kweku-Muata Osei-Bryson: Virginia Commonwealth University
Annette Mills: University of Canterbury

Information Systems Frontiers, 2015, vol. 17, issue 1, No 15, 193-215

Abstract: Abstract Analysing datasets using data mining techniques can enhance decision making in organizations. However, to ensure that the full potential of these techniques is realised it is important that decision makers understand there are Knowledge Discovery and Data Mining (KDDM) processes that are mature enough to be adopted. This paper demonstrates the benefits of using a KDDM process to evaluate survey data for internet banking users in Jamaica which includes demographic as well as attitudinal and behavioral variables. The major benefits of following this process include the selection of a set of models, rather than a single model, which are more relevant to the business/research objectives and use of a more targeted knowledge discovery process as the data mining analyst is now directed to consider the effects the decisions in each phase will have on subsequent phases. This leads to more relevant knowledge being extracted from the data mining process.

Keywords: Knowledge Discovery in Data Mining; Internet banking; Decision tree; Clustering; Demographic and attitudinal profiling (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-012-9397-2

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