AI in Anti-Money Laundering Quiz

Answers and explanations appear as you take the quiz.

1. What operational issue can rule-based transaction-monitoring systems create?
   • They can produce false-positive alerts that require investigator review
   • They establish criminal intent from a transaction amount alone
   • They eliminate all review of customer activity
   • They automatically prove whether funds are illicit
2. What does “structuring” mean under U.S. Bank Secrecy Act guidance?
   • Splitting one account into multiple dashboards for convenience
   • Breaking up transactions for the purpose of evading reporting or recordkeeping requirements
   • Moving a transaction between currencies for an exchange rate
   • Combining several payments for routine bookkeeping
3. Why are graph neural networks especially useful in AML?
   • They translate transaction text into other languages
   • They reduce the cost of storing transaction data
   • They model accounts and transfers as networks, revealing hidden rings of coordinated activity
   • They encrypt all customer communications automatically
4. What is customer due diligence (CDD) information used for in risk-based AML monitoring?
   • Automatically proving that a customer committed a crime
   • Replacing all transaction monitoring
   • Setting a universal threshold for every institution
   • Developing a customer risk profile and assessing activity in context
5. Why is labeled training data a challenge for supervised AML models?
   • Confirmed laundering cases are rare compared to normal transactions
   • Banks refuse to use any historical data
   • Labels change every single day automatically
   • Regulators forbid storing any transaction records
6. Why should an AML alert include an interpretable rationale for an investigator?
   • It makes the alert a final legal finding
   • It helps the investigator review the activity and document why it may be suspicious
   • It removes the need to examine transaction context
   • It guarantees that every alert is true
7. What can entity resolution do in an AML data pipeline?
   • Determine a customer’s guilt without an investigation
   • Change the legal reporting threshold
   • Link records that may refer to the same person or organization across datasets
   • Replace transaction records with generated text
8. How should an investigator interpret an AI-generated transaction risk score?
   • As proof of money laundering
   • As an automatic SAR filing decision
   • As a replacement for the institution’s AML program
   • As a prioritization signal that requires review of supporting activity and context