compliance_monitoring · workflow
Monzo builds a reactive fraud prevention platform with ML controls and DAG feature computation
Fraud is a fast-moving, sophisticated, and highly imbalanced problem: UK losses reached £1.17 billion in 2024, and only 1 in 10,000 Monzo transactions is fraudulent, requiring Monzo to balance intervention accuracy against unnecessary customer friction as fraudsters continually pivot tactics.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Payment triggers platform
When a transaction is made on the Monzo app, the fraud prevention platform begins processing it.
Tools used
GoStarlarkmachine learning models
Outcome
(not stated)
Results
Volume1 in 10,000 transactions
Cost replaced£1.17 billion
Grounding & classification
Source type: technical build writeup
17 fields verified against source quotes.
anomaly detectionfraud detectionpredictive analyticsnamed customerproduction runtime claimedsource backedtools describedworkflow describedbankingtechnical build writeupcompliance monitoringextract classify routemonitor detect alert