Modeling Imbalanced Financial Transactions for Credit Card Fraud Detection Using Machine Learning and Deep Learning Techniques

Abstract

As the growth of electronic commerce and digital payment systems is increasing at a rapid pace, the menace of credit card fraud has surfaced as a highly advanced global threat with a huge financial loss of billions of dollars on a yearly basis. The conventional fraud detection systems using traditional rule-based systems or conventional machine learning techniques have become inadequate in dealing with the huge volume, velocity, and complex nonlinear characteristics of the transactions. This review aims at presenting a comprehensive review of the advanced techniques in the field of credit card fraud detection with special emphasis on the evolution of advanced deep learning techniques from conventional machine learning techniques in the time frame of 2023-2025. It highlights a critical review of conventional machine learning techniques such as Random Forest and Support Vector Machines with advanced deep learning techniques such as Convolutional Neural Network and Long ShortTerm Memory networks. This demonstrates that the results of these individual models face difficulties in handling issues like class imbalance and the need for intensive feature engineering. On the other hand, recent hybrid models, such as the Deep Hybrid CLST model, have shown promising results in handling the problem of detecting financial fraud by integrating spatial feature learning and temporal sequence learning. Furthermore, the review has shown the increasing trend of data silos in the industry due to the enforcement of privacy policies and has introduced Federated Learning and Graph Neural Networks as possible solutions for collaborative learning in the detection of financial fraud. Lastly, the review has shown the difficulties in handling the problem of detecting financial fraud, including the interpretability of the model and scalability in real-time, and has shown the possible future direction of developing stronger and more efficient financial fraud detection systems by focusing on Explainable AI and distributed computing frameworks