Suspicious Activity Investigation Flashcards

What are common transaction monitoring 'red flags' for potential human trafficking proceeds?

Answer: Multiple individuals sharing an address depositing cash with identical amounts, frequent hotel-related charges, purchases at adult entertainment venues, and cash-intensive activity inconsistent with employment or stated income

Human trafficking red flags in financial transactions include: multiple individuals at the same address making similar cash deposits, patterns of hotel, motel, and adult entertainment spending, activity inconsistent with stated employment, and use of prepaid cards or multiple controlled accounts.

What is a 'risk-based transaction monitoring' approach and how does it differ from a 'rules-only' approach?

Answer: Risk-based monitoring calibrates alert thresholds, scenarios, and review intensity based on the customer's risk profile, using both rule-based scenarios and analytical models; rules-only applies uniform detection rules regardless of account risk rating

Risk-based transaction monitoring adjusts detection sensitivity based on the customer's risk profile — higher-risk accounts may have lower alert thresholds or additional scenarios — while pure rules-only approaches apply the same thresholds uniformly regardless of individual account risk.

What specific information should be included in a SAR narrative to maximize its utility to law enforcement?

Answer: The full context of the suspicious activity including who, what, when, where, why it is suspicious, how the scheme operates, all involved parties and accounts, prior SAR history, and any law enforcement contacts or legal process received

An effective SAR narrative answers the five W's plus how: who is involved (all parties and entities), what activity occurred (specific transactions), when (dates and timeline), where (accounts, locations), why it is suspicious (specific reasons), and how the scheme works — providing law enforcement with a complete, actionable intelligence report.

How does 'machine learning' differ from rule-based transaction monitoring in detecting suspicious activity?

Answer: Machine learning models can identify complex, non-linear patterns and previously unknown suspicious behaviors that may not be captured by predefined rules, using historical data to train models that evolve as patterns change

Machine learning can detect novel and complex patterns across large datasets that rule-based systems miss — learning from historical suspicious activity to identify similar but previously unknown behaviors, and adapting as criminal typologies evolve.

What is a 'SAR waiver' and under what circumstances would law enforcement request one?

Answer: A formal request from law enforcement asking a financial institution to temporarily delay or refrain from filing a SAR on a specific account or individual so as not to compromise an active investigation

Law enforcement may request that a financial institution delay or forego SAR filing on a specific account to protect an ongoing investigation — typically through official legal process — ensuring that SAR confidentiality provisions do not inadvertently reveal the investigation to the subject.

What is 'behavioral analytics' in the context of AML transaction monitoring and how does it complement rule-based detection?

Answer: Establishing baseline transaction profiles for individual customers or peer groups, then detecting deviations from those baselines that may indicate suspicious activity — complementing rules by catching gradual behavioral shifts that fall below fixed alert thresholds

Behavioral analytics establishes normal transaction patterns for each customer or peer group and alerts on deviations — catching gradual escalations, low-and-slow structuring, and behavioral shifts that fixed-threshold rules may miss.