-65%
False Positives
From 40% to 14%
-80%
Processing Time
From 48h to 9.6h average
5
FTEs Freed
Reassigned to strategic work
€420K
Annual Savings
Direct cost reduction
Challenge
Manual compliance checks for transaction monitoring required a team of 8 analysts working full-time. False positive rates exceeded 40%, causing delays and customer friction.
Outcome
An AI automation pipeline reduced false positives by 65% and freed 5 FTEs for higher-value compliance work.
Starting Point
A Nordic FinTech company processing over 50,000 transactions a day was dealing with rising compliance costs. Eight full-time analysts ran transaction monitoring, and a 40% false positive rate meant most alerts led nowhere, adding delays that customers noticed.
PSD2 and AML5 requirements meant checks couldn't be cut. The team couldn't hire fast enough to match transaction growth.
Solution
We built an AI automation pipeline with four layers:
- ML pre-screening: models trained on historical data assign a risk score to each transaction before any human reviews it
- RAG-enhanced analysis: a retrieval system pulls relevant regulatory context for flagged transactions
- Human-in-the-loop: analysts see only high-confidence alerts, each with an AI-generated summary and recommendation
- Continuous learning: analyst decisions feed back into the models to improve accuracy over time
Result
We deployed in stages over 6 weeks. False positives fell from 40% to 14%, average processing time dropped by 80%, and 5 analysts moved from routine screening to complex investigations. Annual savings came to €420K.
AI Agent & RAG Developer
AI Agent & RAG Developer with 10+ years of software engineering experience. Specialized in intelligent AI solutions for enterprises in the DACH & Nordic region.