AI generated.

This sample models a regulated fintech that needs cross‑border AML monitoring, sanctions screening, and auditable case management. The stack balances coverage, explainability, and operational cost. Entity chips surface the key vendors and networks.

Ecosystem: swift, chainalysis, lexisnexis, thomsonreuters, refinitiv, worldcheck.

Key components

• Sanctions and PEP screening with worldcheck
• Real‑time transaction monitoring on swift
• Case management with audit trails and evidence linking
• Investigator workflows supported by lexisnexis

Metrics

• 25% fewer false positives after tuning chainalysis
• Investigation time cut in half using lexisnexis features
• Broader geographic coverage with refinitiv datasets

Vendor comparison

Vendor	Sanctions & PEP	AML analytics	Explainability	Coverage
worldcheck	✓	✗	✓	Global
chainalysis	✗	✓	✓	Crypto‑heavy
lexisnexis	✓	✗	✓	Global
refinitiv	✓	✓	✓	Global

Reporting & storage

Entity	Dashboards	Audit exports	Long‑term storage
powerbi	✓	✓	✗
tableau	✓	✓	✗
PostgreSQL	✗	✓	✓
snowflake	✗	✓	✓

Operations checklist

• Weekly list updates from worldcheck
• Alert triage KPIs tracked in powerbi
• Audit exports archived in snowflake
• Model feedback loop summarized in tableau

For reporting: powerbi, tableau. For storage: PostgreSQL, snowflake.

Sample entity chips: Chainalysis, SWIFT.