AML & Fraud Fundamentals
Concepts and workflows for anti-money-laundering screening and fraud detection across onboarding and ongoing monitoring.
Who this is for
- Compliance analysts.
- Fraud and risk teams.
- Builders integrating screening and fraud signals.
Lessons
1. Screening landscape
- Sanctions lists and embargoes.
- Politically Exposed Persons (PEP).
- Internal and external watchlists.
- Adverse media and reputational signals.
2. Match quality
Strong matching reduces false positives without missing true risk. Name, date of birth, country, and identifier alignment all influence confidence. Phonetic and translation considerations matter for global coverage.
3. Fraud patterns
- Synthetic identity (fabricated person).
- Account takeover (compromised credentials).
- Mule accounts (used for layering).
- Coordinated rings (device, IP, or behavior clusters).
4. Graph intelligence
Networks reveal patterns that single-record analysis misses. Linking persons, devices, accounts, wallets, and transactions exposes coordinated fraud and laundering.
5. Case workflow
Alerts are not decisions. Triage, escalation, investigation, and disposition produce auditable case outcomes with regulator-ready evidence.
Applied scenarios
- A potential PEP match requires enhanced due diligence.
- A device fingerprint links five new applications.
- A transaction pattern suggests layering across mobile money and bank rails.
Review checkpoint
You should be able to:
- Distinguish sanctions from PEP from adverse media.
- Explain why graph analysis catches what single-record review cannot.
- Describe the lifecycle of an alert from creation to disposition.