Neural data mining for credit card fraud detection

Abstract

The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: since only one financial transaction in a thousand is invalid no prediction success less than 99.9% is acceptable. Because of these credit card transaction requirements, completely new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and a neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.