Credit card fraud detection using naive Bayesian and C4.5 decision tree classifiers

Abstract

Growing problem of card payment fraudulent abuse is a main focus of banks and payment Service Providers (PSPs). This study is using naive Bayes, C4.5 decision tree and bagging ensemble machine learning algorithms to predict outcome of regular and fraud transactions. Performance of algorithms is evaluated through: precision, recall, PRC area rates. Performance of machine learning algorithms PRC rates between 0,999 and 1,000 expressing that these algorithms are quite good in distinguishing binary class 0 in our data set. Amongst all algorithms best performing PRC class 1 rate has Bagging with C4.5 decision tree as base learner with rate of 0,825. For prediction of fraud transactions with success of 92,74% correctly predicted with C4.5 decision tree algorithm.