The problem
Fraud queues drown analysts in raw signals: device data, velocity counters, history spread over five tools. Review time per case stays high, so thresholds stay loose or backlogs grow.
The system
A triage layer where deterministic rules and anomaly scores decide what enters the queue, and an AI case-builder compiles each flagged account into a structured brief — signals, history, similar past cases, and a recommended action with cited evidence. The model never blocks anyone; it prepares the decision.
How it's built
- Feature pipeline over transactions/devices/history; explainable scores
- LLM case-file generation with strict citation to underlying records
- Reviewer UI with one-click outcomes feeding back into rules
- Full decision audit trail for compliance
Delivery
Sprint on historical cases measures triage accuracy and time-saved; Build wires it into your live queue.
What to expect
- Review time per case typically cut by half or more
- Consistent, documented decisions — every action has a case file
- Human authority preserved: AI drafts, analysts decide
Documented results in the wild
Independent, published deployments of this class of system — cited as market evidence that it works at scale. These are not our clients.
- HSBC ML transaction monitoring finds 2–4× more suspicious activity than the prior rules-based system while cutting alert volumes 60%, across 1.2B+ transactions/month. Google Cloud, 2023 ↗
- Revolut AI-based fraud detection plus a 24/7 financial-crime team prevented over £475M of potential fraud against customers in 2023. Revolut Financial Crime Report, 2024 ↗