Association Rules and Predictive Models for e-Banking Services

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. In addition, the development and continuous training of prediction models is a very significant task, especially for bank organizations. The establishment of such models with the capacity of accurate prediction of future facts enhances the decision making and the fulfillment of the bank goals, especially in case these models are applied on specific bank units. E-banking can be considered such a unit receiving influence from a number of different sides. Scope of this paper is the demonstration of the application of data mining methods to ebanking. In other words association rules concerning e-banking are discovered using different techniques and a prediction model is established, depending on e-banking parameters like the transactions volume conducted through this alternative channel in relation with other crucial parameters like the number of active users.