Fraud Classification in Online Payments Using Supervised Machine Learning Algorithms

Online payment systems have become a cornerstone of modern financial transactions, providing convenience and efficiency. However, the rise of such systems has also led to an increase in fraudulent activities, posing significant risks to users and service providers. This research focused on optimizing the classification of fraudulent transactions in online payment systems using supervised machine learning algorithms. This study explored the performance of several widely used algorithms, including Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting Tree, and Support Vector Machine (SVM). A comprehensive dataset of online payment transactions was used to evaluate the effectiveness of these algorithms in identifying fraudulent activities. Various performance metrics, such as accuracy, precision, and F1 score, were employed to assess and compare classification capabilities. In addition, feature engineering and data preprocessing techniques were applied to improve the models’ predictive performance. The results demonstrated that, while each algorithm had its strengths, ensemble-based methods like Gradient Boosting Tree outperformed others in terms of classification accuracy and robustness. The findings highlighted the importance of selecting appropriate machine learning algorithms and fine-tuning their parameters to achieve optimal fraud detection in online payment systems. This study provides valuable insights for financial institutions and developers to enhance security measures and mitigate fraud risks in digital payment platforms.