Investigation of Data Mining Techniques in Fraud Detection: Credit Card

Abstract

In recent times the more secure data transfer takes place almost by means of internet. Apart from the corporate companies, publics also started using the network media. At the same time the risk also increases in secure data transfer. One of the major issue among them is credit card fraud detection systems which has a significant percentage of transactions labeled as fraudulent are in fact legitimate. Thus this may delay the fraudulent transaction detection. Due to ever increasing volumes of data needed to be analyzed using data mining methods and techniques which are being used more and more. The aim of this study is to analyze the five most frequently used classification techniques in fraudulent detection. Neural Network, Decision Tree, Naïve Bayes, K-nn and Support Vector Machine are taken in to consideration. This paper discusses on each techniques and their limitations. Still they suffer from the problem of false detection rate highly. Keywords: