Detecting Fraud Transaction using Ripper Algorithm Combines with Ensemble Learning Model

Abstract

In the context of the 4.0 technology revolution, which develops and applies strongly in many fields, in which the banking sector is considered to be the leading one, the application of algorithms to detect fraud is extremely important. necessary. In recent years, credit card transactions including physical credit card payments and online payments have become increasingly popular in many countries around the world. This convenient payment method attracts more and more criminals, especially credit card fraud. As a result, many banks around the world have developed fraud detection and prevention systems for each credit card transaction. Data mining is one of the techniques applied in these systems. This study uses the Ripper algorithm to detect fraudulent transactions on large data sets, and the results obtained with accuracy, recall, and F1 measure of more than 97%. This research then used the Ripper algorithm combined with Ensemble Learning models to detect fraudulent transactions, the results are more than 99% reliable. Specifically, this model using the Ripper algorithm combined with the Gradient Boosting method has improved the predictive ability and obtained very reliable results. The use of algorithms combined with machine learning models is expected to be a new topic and will be widely applied to banks' or organizations' activities related to e-commerce.