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

Gunjan Mansingh

doi:10.1007/s10796-012-9397-2

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

2013-01-19

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G

Gunjan Mansingh *

K

Kweku-Muata Osei-Bryson

A

Annette Mills

DOI：10.1007/s10796-012-9397-2

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Abstract

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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

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

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