Why 90% of Sanctions Screening Alerts Are False Positives (And How to Fix It with Python)

Why 90% of Sanctions Screening Alerts Are False Positives (And How to Fix It with Python) If you've ever built a KYC or AML compliance pipeline, you already know the pain: your sanctions screening system fires an alert for "Mohammed Al Hassan" and your compliance team spends two hours manually verifying it's not that Mohammed Al Hassan on the OFAC SDN list. Then it happens again. And again. Industry data consistently shows that 90%+ of sanctions screening matches are false positives . This is not just an annoyance — it creates alert fatigue, slows down customer onboarding, and costs fintech teams real money in manual review time. In this tutorial, we will look at why traditional approaches fail and how to build a smarter Python sanctions screening pipeline that dramatically reduces false positives. Why Traditional Name Matching Fails Most legacy sanctions screening tools rely on simple string-matching algorithms like Levenshtein distance or Jaro-Winkler similarity. Here is what that lo