The Impact of Machine Learning Algorithms on Credit Card Fraud Detection: A Comparative Study

Credit card fraud detection is a critical challenge for financial institutions, with billions of dollars lost annually due to fraudulent activities. Traditional rule-based systems struggle to keep up with evolving fraud patterns, often producing high false positive rates. This study presents a comparative analysis of machine learning algorithms for credit card fraud detection, focusing on their accuracy, efficiency, and real-time detection capabilities. Using a highly imbalanced dataset containing 284,807 transactions, we evaluated the performance of Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Gradient Boosting Machines (GBMs). The results demonstrate that ensemble methods, particularly GBMs such as XGBoost, outperform simpler models in terms of accuracy and recall, effectively handling the imbalanced data. ANNs also showed promising results but require significant computational resources. The study highlights the need for a balance between accuracy, processing speed, adaptability, and scalability in real-world fraud detection systems. Future research should explore advanced deep learning models, continuous learning, transfer learning, and explainable AI approaches to enhance the transparency and trust in automated fraud detection systems.