Definition
Alerts triggered for legitimate activity. The system identifies something as suspicious when it's actually normal.
AML Operations
Understanding False Positives in AML
Understanding false positives in AML transaction monitoring. Learn causes, impacts, reduction strategies, and how to improve alert quality.
Key Information
The false positive challenge
False positives are alerts generated by AML systems for transactions that appear suspicious but are actually legitimate.
Managing false positives is critical—too many overwhelm analysts, while overly strict filtering risks missing real suspicious activity.
Understanding and managing false positives
Common Causes
Overly broad rules, outdated thresholds, insufficient customer context, data quality issues.
Impact
Analyst fatigue, delayed investigation of real issues, increased operational costs, customer friction.
Reduction Strategies
ML-based scoring, rule tuning, customer segmentation, feedback loops, better data integration.
Frequently asked questions
What is an acceptable false positive rate?
Industry varies widely, but many organizations see 90%+ false positive rates. Leading firms reduce this to 50-70% through optimization.
How do false positives affect compliance?
High false positive rates drain investigation resources, potentially causing real suspicious activity to be missed or delayed.
Can AI eliminate false positives?
AI significantly reduces false positives through pattern recognition, but cannot eliminate them entirely. Human review remains necessary.
Simplify Your Compliance
ARCaml helps Australian businesses meet AML/CTF obligations.