RFM analysis for decision support in e-banking area

The introduction of data mining methods in the banking area due to the nature and sensitivity of bank data can already be considered of great assistance to banks as to prediction, forecasting and decision support. Concerning decision making, it is very important a bank to have the knowledge of (a) customer profitability and customers’ grouping according to this parameter and (b) association rules between products and services it offers in order to more sufficiently support its decisions. Object of this paper is to demonstrate that keeping track of customer groups according to their profitability and discovery of association rules between products and services is of major importance as to its decision support. Key-words: Data Mining, Decision Support, Association Rules, RFM analysis, Clustering