AI Financial-Crime Analyst
Build a bank-grade AML system: monitor transactions, resolve beneficial-ownership graphs, screen sanctions/PEP, detect laundering typologies, and draft SARs behind a human approval gate.
Modules
6
Verified
0
In Progress
0
What you walk away with
- ✓A shareable portfolio URL with your project walkthrough
- ✓Module-by-module timeline of everything you built
- ✓All the code — pipelines, guardrails, deployment configs
- ✓Production patterns documented on your profile
Financial-crime teams drown in false positives while real laundering slips through. In this course you build an AI Financial-Crime Analyst end to end: ingest transactions, KYC entities, and sanctions/PEP watchlists; resolve accounts into beneficial-ownership entity graphs; fuzzy-screen names and tune the false-positive problem; detect AML typologies (structuring, layering, rapid movement, round-tripping) measured recall-first against labeled ground truth; and ship an agent that triages each alert and drafts a Suspicious Activity Report behind a human approval gate, with a full audit trail. Ends as a case-review UI plus a monitoring dashboard. Every module produces working code.