A comprehensive review of machine learning techniques for detecting fraud in banking and payment services

In today's digital age, fraud detection in financial services has become essential due to the rapid growth and complexity of online transactions. Machine learning techniques are widely used to detect unusual activities in real time. This paper focuses on fraud in banking and payment services and proposes a three-stage review framework: formulating research questions, defining the research methodology, and analysing existing literature. The review reveals that supervised and unsupervised learning algorithms, such as Naïve Bayes, K-nearest neighbours, deep learning, support vector machine, decision tree, artificial neural network, XGBoost, and AdaBoost, are commonly applied for fraud detection. These models are evaluated using metrics like precision, recall, and F-score. Ensemble methods that combine multiple algorithms are also shown to improve detection accuracy. Finally, the review highlights future research directions, especially the need to strengthen wallet payment systems by developing more robust and efficient fraud detection algorithms to ensure secure digital transactions.