Guide-yakabatana mibvunzo · Pakati Nhanho

AI muAnti-Money Laundering Quiz

Review how AI can support transaction monitoring, customer-risk assessment, graph analysis, and human review in anti-money-laundering work.

Nzira dzekutungamira dzinoenderanaAI MuAnti Mari Kubira
Mubvunzo 1 ye 8

What operational issue can rule-based transaction-monitoring systems create?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  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. Sei magirafu neural network achinyanya kubatsira mu AML?
    • Vanoshandura zvinyorwa zvekutengeserana mune mimwe mitauro
    • Vanoderedza mari yekuchengetedza data yekutengeserana
    • Ivo vanoenzanisira maakaunti uye kuchinjirwa setiweki, vachiburitsa zvakavanzika zvindori zvebasa rakarongeka
    • Ivo encrypt zvese mutengi kutaurirana otomatiki
  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. Nei yakanyorwa dhata yekudzidziswa iri dambudziko kune inotariswa AML modhi?
    • Mhosva dzembatya dzakasimbiswa hadziwanzo kuenzanisa nekutengeserana kwakajairika
    • Mabhangi anoramba kushandisa chero nhoroondo data
    • Mazita anochinja otomatiki zuva rega rega
    • Vatongi vanorambidza kuchengetedza chero zvinyorwa zvekutengeserana
  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