kyc_aml · workflow
Monzo builds fraud detection ML systems nominated for Outstanding Prevention Initiative, with feature store x3000 data ingestion gain
Multiple types of financial fraud were causing devastating harm to customers and significant financial losses for Monzo, requiring a systematic ML-driven approach to detection and prevention.
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 · Live transaction triggers inference
Making a prediction on a transaction is described as the canonical live inference use case driving the fraud detection workflow.
Tools used
GCP AI PlatformAWSPython
Outcome
Monzo's fraud detection system made an enormous dent in financial crime and was nominated for Outstanding Prevention Initiative at the 2021 Tackling Economic Crime Awards; its internal feature store was optimized to x3000 faster data ingestion, powering six critical ML systems across the company.
Results
Volumex3000
Cost replacedenormous dent into this problem
Running sinceearly 2020
Grounding & classification
Source type: technical build writeup
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anomaly detectioncomputer visiondocument classificationfraud detectionpredictive analyticssupport tickethuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedbankingaccuracy improvementthroughput increasetechnical build writeupcompliance monitoringcustomer supportkyc amlextract classify routemonitor detect alert