A Survey on Online Payment Fraud Detection Techniques using Machine Learning Algorithms

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Online transaction fraud detection has become a critical challenge with the rise of digital payment systems. This paper surveys various machine learning techniques employed in fraud detection, including Support Vector Machines (SVM-QUBO), Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, Decision Trees, and Random Forest. The performance of each method is evaluated in terms of accuracy, precision, recall, and computational efficiency. The study also explores how different algorithms handle high-dimensional, imbalanced datasets and the impact of feature selection techniques. The results provide insights into the strengths and limitations of these algorithms, offering a comprehensive comparison for fraud detection in digital transactions