Eight ML-driven risk factors. One composite score.
The core AML risk engine. Scores every US zip code across drug trafficking, financial crime designations, border proximity, industry risk, and trade-based money laundering.
What GeoAML covers
Each factor contributes to a composite GeoAML Risk Score and Level, calculated from machine learning, curated intelligence, and expert features.
HIDTA
High Intensity Drug Trafficking Areas designated by ONDCP. Scored at zip-code level, not county.
HIFCA
High Intensity Financial Crime Areas with elevated money laundering activity.
Geographic Targeting Orders
FinCEN-issued orders imposing additional reporting on specific areas.
Southwest Border
Distance to the US-Mexico border. Primary corridor for drug smuggling.
Drug Trafficking Risk
ML-derived risk within HIDTA and non-HIDTA regions. 5-tier classification.
Industry Risk
NAICS-based industry risk. MSBs, CIBs, NBFIs, TPPPs, and more.
International Nexus
Cross-border payment probability risk indicators.
Trade-Based Money Laundering
TBML vulnerability. Import/export concentrations near ports and airports.
Why zip-code-level ML matters
Traditional: county-level HIDTA
Binary flag — entire county is HIDTA or not. No differentiation between high-crime urban zip and quiet suburban one. Massive over-alerting.
GeoAML: zip-code ML scoring
5-tier risk classification at zip level using ML across the KYG framework. Processes 1B+ data points for granular, defensible risk scores.
See GeoAML in action
Request a demo to see how zip-code-level ML scoring compares to your current approach.