Predictive Models and Knowledge Management in e-Banking Data

ABSTRACT Knowledge Management exercises significant influence in establishment and development of a company. A modern such approach is data mining. Data mining is the search for relationships and patterns that exist in data sets, but are "hidden" among the vast amounts of data. These relationships and patterns represent valuable knowledge about the data set. Through data mining methods, certain variables obtained from observation can be represented by means of various models like neural networks and decision trees. Many companies and organizations use nowadays such tools, that contribute to the more effective control and exploitation of their knowledge and information. In the present paper prediction models, a popular data mining method is studied, by the use of an example coming from the real world and specifically electronic banking. It is demonstrated that prediction models contribute to the more efficient knowledge management in electronic banking sector

Data Mining for Decision Support in e-banking area

ABSTRACT 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 their 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 it offers to those groups, is of major importance as to its decision support