Deep Learning Approaches for Identifying Fraudulent Activities in Digital Payment Networks

Abstract

The migration of commerce onto digital payment networks has been accompanied by an escalation in the scale, speed, and sophistication of financial fraud, exposing the limits of rule-based systems and classical machine-learning classifiers that score each transaction in isolation. Over the past decade, deep learning has emerged as the dominant methodological response, offering models that learn discriminative patterns, temporal dynamics, latent normality, and relational structure directly from transaction data. This article provides a comprehensive review of deep learning approaches for identifying fraudulent activities in digital payment networks, organised as a taxonomy of architectural families and a comparative analysis of their respective strengths and limitations. We examine discriminative architectures (feedforward, convolutional, and recurrent networks); reconstruction- and generation-based approaches (autoencoders, variational autoencoders, and generative adversarial networks) used for anomaly detection and for synthesising minority-class data; attention-based transformers that model long-range behavioural dependencies; graph neural networks that capture the relational structure of organised fraud rings; and the hybrid and ensemble systems that increasingly combine these components with gradient-boosted trees. Across this taxonomy we identify a set of cross-cutting challenges that determine practical success more than any single architecture: extreme class imbalance, non-stationary fraud driven by adversarial adaptation, the latency constraints of real-time authorisation, the privacy limits on data sharing, and the demand for explainable decisions in a regulated domain. Synthesising reported benchmark evidence, we find that no single architecture dominates universally; that sequence-aware and relational models offer the clearest advantage where behavioural and network signal is present; that imbalance handling and deployment engineering frequently matter more than model choice; and that hybrid pipelines presently represent the strongest practical solutions. We conclude that the field is converging on hybrid, imbalance-aware, explainable systems, and that the decisive research frontier lies less in raw detection accuracy than in robustness, real-time viability, privacy preservation, and interpretability.