Machine Learning for Credit Card Fraud Detection

Abstract

E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Many errors are lost due to fraudulent card transactions each year. The development of performance fraud detection methods is obligatory to minimize such losses. This article examines the usefulness of applying different learning approaches for detecting credit card fraud. Three algorithms will be applied on a database of an American bank and the data will be exploited based on supervised and unsupervised learning techniques, namely the MLP (multilayer perceptron), LR (logistic regression) and PCA (Principal Component Analysis). The main purpose of the study is comparing the classification performance of each algorithm using real dataset of fraudulent user accounts in a telecommunication network.