aceneural network architectures for real-time financial fraud detection in payment systems
Source article: <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…
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PIX - explaining a state-owned Fintech by MARIO G. SCHAPIRO PEDRO SALOMON BEZERRA MOUALLEM ERIC GIL DANTAS. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0
As of its March 5 2026 publication, this narrative literature review surveyed how neural network architectures are used for real-time financial fraud detection, covering MLPs, LSTMs, CNNs, Autoencoders, GNNs and Transformers, and the production requirement to operate within sub-100-millisecond payment authorization pipelines, with examples from credit card networks and Brazil's PIX system.
It matters because conventional methods have failed to contain $485.6 billion in annual losses estimated for 2023, while neural approaches introduce new trade-offs around adversarial robustness, explainability under GDPR and LGPD, and privacy-preserving techniques like Federated Learning; it remains uncertain which hybrid combinations will generalize across fraud typologies under live drift and evasion.
- Review covers MLPs, LSTMs, CNNs, Autoencoders, GNNs, and Transformer-based models for fraud detection.
- Production deployment constrained by payment authorization pipelines operating under sub-100-millisecond latency budgets.
- Real-world implementations documented across credit card networks, Brazil's PIX instant payment system, anti-money laundering, and insurance fraud.
- Operational challenges include concept drift, adversarial evasion attacks, class imbalance, and GDPR and LGPD explainability requirements.
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.
The rundown
The review examines theoretical foundations and six principal architectures: Multilayer Perceptrons, Long Short-Term Memory networks, Convolutional Neural Networks, Autoencoders, Graph Neural Networks, and Transformer-based models, plus infrastructure for payment authorization.
It documents implementations in credit card networks, instant payment systems with particular attention to Brazil's PIX ecosystem, anti-money laundering operations, and insurance fraud, while noting frontier directions like online learning, LLMs as orchestrators, generative AI weaponized by fraudsters, and Quantum Machine Learning.
No single neural architecture dominates across all fraud typologies, indicating performance varies by fraud type and hybrid approaches are required.
Sources
- Peer-reviewedJournal International Review of Research Studies2026-03-05
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