Credit Card Fraud Detection Using Random Forest Algorithm

Abstract

Credit card fraudulent happens through the account holders card number, card details and personal information. E-commerce payment system is providing the payment for online transaction. The model is used to identify whether a new transaction is fraudulent or not. Aim is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. A standard scalar model is initially trained with the normal behavior of a card holder. If an incoming credit card transaction is not accepted by the trained standard scalar model with sufficiently high probability, it is considered to be fraudulent, which defines a plot of test perception as the y coordinate versus its 1-specificity or false positive rate (FPR) as the x coordinate, is an effective method of estimate the quality or performance of diagnostic tests. The significance of the application technique reviewed in the minimization of credit card fraud. Still some issues when genuine credit card customers are misclassified as fraudulent. SMOTE is a statistical technique for increasing the number of cases in your dataset in a balanced way. Random forest builds multiple decision trees and integrate them together to get stable prediction and accuracy of about 98.6%.