  Clustering analysis and Datamining methodologies were applied to the problem
of identifying illegal and fraud transactions. The researchers independently
developed model and software using data provided by a bank and using Rapidminer
modeling tool. The research objectives are to propose dynamic model and
mechanism to cover fraud detection system limitations. KDA model as proposed
model can detect 68.75% of fraudulent transactions with online dynamic modeling
and 81.25% in offline mode and the Fraud Detection System & Decision Support
System. Software propose a good supporting procedure to detect fraudulent
transaction dynamically.
