Author(s)

V. Aggelis

Abstract

The introduction of data mining methods in the banking area although conducted in a slower way than in other fields, mainly due to the nature and sensitivity of bank data, can already be considered of great assistance to banks as to prediction, forecasting and decision making. One particular method is the investigation for association rules between products and services a bank offers. Results are generally impressive since in many cases strong relations are established, which are not easily observed at a first glance. These rules are used as additional tools aiming at the continuous improvement of bank services and products helping also in approaching new customers. The scope of this paper is the presentation of a model which discovers and determinate such rules concerning e-banking using different methods. Keywords: data mining, e-banking, association rules, a priori, generalized rule induction. 1 Introduction Determination of association rules concerning bank data is a challenging though demanding task since: • The volume of bank data is enormous. Therefore the data should be adequately prepared by the data miner before the final step of the application of the method. • The objective must be clearly set from the beginning. In many cases not having a clear aim results in erroneous or no results at all. • Good knowledge of the data is a prerequisite not only for the data miner but also for the final analyst (manager). Otherwise wrong results will be produced by the data miner and unreliable conclusions will be drawn by the manager.

Keywords

data mining, e-banking, association rules, a priori, generalized rule induction.