ace<b>NEURAL NETWORKS FOR REAL-TIME FINANCIAL FRAUD DETECTION</b>
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…
Hybrid and ensemble neural network frameworks that combine temporal, relational, and anomaly-detection capabilities consistently achieve superior real-time fraud detection performance in credit card networks and instant payment systems.
Neural network fraud detection systems in production face concept drift, adversarial evasion attacks, class imbalance, GDPR and LGPD explainability requirements, and sub-100-millisecond latency budgets that constrain deployment.
No single neural architecture dominates across all fraud typologies, indicating performance varies by fraud type and hybrid approaches are required.
Evidence
- Peer-reviewedJournal International Review of Research Studies2026-03-05
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Truvace Impact Record TRV-2026-0339, v1: “<b>NEURAL NETWORKS FOR REAL-TIME FINANCIAL FRAUD DETECTION</b>.” Truvace, 2026-07-20. /record/TRV-2026-0339 (accessed at citation time). sha256 23a1f2620898295f…
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