Towards modeling financial fraud detection using radial basis function – artificial neural network

Abstract

The widespread cases of financial fraud are assuming an alarming and frightening proportion. Fraud detection and prevention mechanisms are concurrent processes in combating fraud malaise. The hitherto traditional methods of fraud detection are not enough to deal with the present level of sophistry with which financial fraudulent acts are perpetrated. We propose a framework for the design of an enhanced fraud detection model using an ensemble radial basis function and artificial neural networks. This research provides a proactive rather than a reactive measures to fraud detection and would found relevance among corporate business professionals and government agencies, thereby minimizing the time and cost of fraud detection.