NEURAL NETWORKS FOR REAL-TIME FINANCIAL FRAUD DETECTION

Abstract

The accelerating digitalization of financial services has transformed fraud into a systemic global threat, with annual losses estimated at $485.6 billion in 2023 alone — losses that conventional rule-based and statistical detection methods have proven structurally incapable of containing. This article presents a narrative literature review examining how neural network architectures are redefining real-time fraud detection in financial systems. The review covers the theoretical and epistemological foundations of the field, the principal neural architectures deployed — including Multilayer Perceptrons (MLPs), Long Short-Term Memory networks (LSTMs), Convolutional Neural Networks (CNNs), Autoencoders, Graph Neural Networks (GNNs), and Transformer-based models — and the production infrastructure requirements that constrain their deployment within payment authorization pipelines operating under sub-100-millisecond latency budgets. Critical operational challenges are analyzed in depth, including concept drift, adversarial evasion attacks, class imbalance, algorithmic explainability under GDPR and LGPD regulatory frameworks, and the data privacy constraints that motivate Federated Learning and Differential Privacy approaches. Real-world implementations across credit card networks, instant payment systems — with particular attention to Brazil's PIX ecosystem — anti-money laundering operations, and insurance fraud are documented. The review concludes by mapping the research frontier, encompassing online learning, Large Language Models as fraud detection orchestrators, generative AI weaponized by fraudsters, and the long-term potential of Quantum Machine Learning. Findings indicate that while no single architecture dominates across all fraud typologies, hybrid and ensemble frameworks combining temporal, relational, and anomaly-detection capabilities consistently achieve superior performance, and that the integration of regulatory compliance, explainability, and adversarial robustness alongside predictive accuracy represents the defining challenge for the next generation of production fraud detection systems.