Leveraging the State-of-the-Art Machine-Learning Techniques for Amplifying the Credit Card Fraud Detection

Abstract

In this paper mainly focused on credit card fraud detection for in real world. Initially collect the credit card datasets for trained dataset. Then will provide the user credit card queries for testing data set. After classification process of random forest algorithm using to the already analyzing data set and user provide current dataset. Finally optimizing the accuracy of the result data. Then will apply the processing of some of the attributes provided can find affected fraud detection in viewing the graphical model visualization. The performance of the techniques is evaluated based on accuracy, sensitivity, and specificity, precision. The results indicate about the optimal accuracy for Decision tree are 98.6% respectively. Keywords: credit card fraud detection, random forest algorithm, fraud detection, visualization, Decision tree.