Enhancing Credit Card Fraud Detection using Ensemble Techniques

Abstract: Credit card fraud is one of the major detriments and a growing concern in the financial sector, causing enormous financial loss and a breakdown in confidence in digital payment systems. The motivation of this paper is to solve the challenge of having an accurate detection of fraudulent transactions and reducing false positives that inconvenience valid customers and strain financial institutions. The challenge is that there is an increase in sophistication regarding the techniques, which makes fraud hard to detect with traditional systems. It proposes an ensemble machine learning technique for credit card fraud detection, aiming at enhancing accuracy and the robustness of fraud detection systems. In this paper, we implement our methodology on a publicly available credit card transaction dataset with different ensemble learning models, namely, Random Forests, Gradient Boosting Machines, and XGBoost. We compare the performance of such models with classical machine learning approaches in terms of accuracy, precision, recall, and F1-score in order to decide their efficiency in detecting fraud cases. Our results prove that the performance of ensemble methods is much better as compared to individual machine learning models, therefore giving a high rate of detection of fraudulent transactions with lower false positive rates. For that reason, the implication of this research is very serious, as it will provide a better and more effective fraud-detection method for financial organizations that might prevent huge losses and inspire customer confidence by minimizing service disruption due to false alarms.