Acams CGSS Exam Questions

Under the OFAC 50% Rule, an entity must be treated as a Specially Designated National (SDN) --- even if not named on the SDN List --- when:

One SDN owns 50% or more of the entity, OR

Multiple SDNs collectively own 50% or more of the entity (ownership must be aggregated).

Applying this rule:

A: 98% ownership by a single SDN Blocked (exceeds 50%).

B: 35% + 15% = 50% aggregate ownership by two SDNs Blocked (meets 50%).

C: 20% + 25% = 45%, which does not meet the threshold Not blocked.

D: 12% + 18% + 28% = 58% aggregate ownership by SDNs Blocked (exceeds 50%).

E: 45% + 3% = 48%, which is below 50% Not blocked.

F: 10 SDNs 4% = 40%, which is below 50% Not blocked.

Thus, only A, B, and D meet or exceed the 50% aggregate ownership requirement defined by OFAC.

OFAC ''50 Percent Rule'' Guidance: Entities owned 50% or more, directly or indirectly, singly or in the aggregate by one or more SDNs, are considered blocked.

OFAC Ownership and Control Interpretive Guidance detailing aggregation of SDN ownership percentages.

Transliteration is defined as converting writing from one script into another while preserving pronunciation as closely as possible. In sanctions screening, transliteration is important because sanctioned parties may appear in multiple scripts (e.g., Cyrillic, Arabic, Mandarin) and the automated screening system must recognize these variations.

While option B focuses specifically on converting non-Latin scripts into Latin, sanctions screening requires a broader definition that applies to any script-to-script conversion relevant to matching names globally. Thus, the correct definition is the general conversion of text from one script into another.

Reference from Sanctions and Compliance Domains:

Definition of transliteration in sanctions screening and name-matching methodologies.

Need for script conversion to support accurate detection of sanctioned parties across language systems.

Artificial intelligence (AI) enhances sanctions screening by improving:

* name-matching accuracy,

* pattern recognition,

* reduction of false positives, and

* detection of complex sanctions-evasion typologies.

AI can analyze large volumes of data in real time and identify subtle risk indicators which traditional systems may miss.

Cryptocurrency mining tools and anonymizing tools hinder compliance, while tuning adjusts system thresholds but is not a standalone technology.

Use of AI for sanctions screening optimization.

Machine-learning applications to sanctions list matching and alert quality.

A historical review or lookback is warranted when previously unidentified data elements, missing reference data, or newly discovered customer attributes are detected that may have impacted earlier screening results. Sanctions and Compliance Domains explain that such scenarios indicate that past alerts may not have been correctly generated or evaluated, necessitating a retrospective review.

Routine list updates or daily refreshes do not automatically trigger lookbacks. New sanctions programs may require enhanced forward-looking monitoring, but only missing or newly uncovered data affecting past screening results justify a historical lookback.

Requirements for lookbacks when gaps in screening data are discovered.

Identification of previously missing identifiers as a trigger for retrospective review.

EU best practices allow processing of payments that fall under permitted exemptions such as basic needs and reasonable living expenses. These may include essential subscriptions such as newspapers or communication services, provided proper authorization or notification is followed.

Salary payments generally require strict conditions and may not be allowed directly for designated persons. Taxi rides and luxury purchases are not considered basic or essential needs and cannot be processed.

EU restrictive measures exemptions for basic needs and essential services.

Conditions for processing payments for designated persons.