Enhanced credit card fraud detection in online transactions using Long Short Term Memory (LSTM) and RFM analysis with ADAYSN oversampling

Abstract

The expansion of online transactions, particularly online credit card transactions, has revolutionized the field of e-commerce and streamlined electronic payment systems. However, this growth has also given rise to a significant challenge in the form of credit card fraud. To combat this issue, banks and financial organizations have recognized the need for robust credit card fraud detection applications. Machine learning (ML) approaches have emerged as a valuable tool in this regard, as they offer the potential to accurately detect and prevent fraudulent transactions. Long Short-Term Memory (LSTM), a recurrent neural network, is used in this study’s evaluation of ML approaches for detecting credit card fraud (CCFD) in online transactions. The most effective LSTM architecture is chosen after a thorough examination based on its capacity to identify credit card fraud with high accuracy and precision. The suggested method makes use of LSTM and RFM analysis to comprehend customer behavior and ADASYN sampling to address class imbalance. The findings show that the selected LSTM architecture, in combination with RFM analysis and ADASYN, delivers great efficiency and efficacy in identifying credit card fraud, hence promoting safe online transactions.