AI Fraud Detection in Real-Time Payments: Architecting ML That Catches Attacks in Milliseconds

If you are building or operating a real-time payments platform in 2026, you already know that rules-based fraud detection is dead. Attackers use generative AI to produce thousands of synthetic identities per hour, payment rails like FedNow and SEPA Instant settle in under 10 seconds, and authorised push payment (APP) fraud alone cost UK consumers £460 million in 2025 according to UK Finance. The only way to keep up is machine learning operating at the wire — feature computation, model inference, and a decision returned before the payment completes. The modelling is the easy part. The hard part is building the infrastructure to serve those models at sub-100-millisecond latency, with features computed from streaming data, with rules and models running in the same decision pipeline, and with explainability that satisfies regulators. ...

July 27, 2026 · 8 min · jnas

Edge Computing for Financial Services: Low-Latency Processing at the Network Edge

Edge computing brings processing closer to the data source. For financial services, this means processing transactions, analysing risk, and detecting fraud at the network edge — closer to customers, exchanges, and data sources — rather than in a centralised cloud data centre. Edge computing is not new. Trading firms have colocated with exchanges for decades to minimise latency. But the cloud providers’ edge offerings — AWS Wavelength, GCP Distributed Cloud, Azure Edge Zones — have made edge computing accessible to a broader range of financial services firms. ...

April 10, 2024 · 4 min · jnas