Fraud Detection in Digital Payments: A Systematic Literature Review of Machine Learning Approaches

Abstract

Digital payment platforms now process trillions of transactions annually, and the volume of associated fraud has grown in step with this expansion, with global card-fraud losses estimated in the tens of billions of dollars per year. Rule-based fraud controls, once the industry standard, are increasingly unable to keep pace with adaptive fraudsters and the scale of modern transaction streams. This paper presents a systematic literature review of machine learning (ML) approaches to fraud detection in digital payment systems. Drawing on peer-reviewed studies published between 2016 and 2025, the review organises the field into four broad technique families - classical supervised learning, deep and sequence-based learning, unsupervised and hybrid anomaly detection, and ensemble/reinforcement-learning architectures - and examines the datasets, evaluation metrics, and class-imbalance strategies that recur across the literature. The review finds that while deep sequential models and hybrid ensembles report the strongest detection accuracy on benchmark datasets, most published work still relies on a small number of public, heavily anonymised datasets, limiting generalisability to live payment environments. The paper concludes by proposing a conceptual, layered fraud-detection framework that synthesises the strengths identified in the literature and by outlining open research challenges, including concept drift, explainability, adversarial robustness, and privacy-preserving cross-institutional learning. Keywords: fraud detection, digital payments, machine learning, deep learning, anomaly detection, credit card fraud, fintech security