Sanctions match explanation template

Why did this customer match a sanctions list entry? A sanctions match explanation answers that for one screening hit: each identifying field side by side, where they agree or differ, how strong each identifier is, and what would settle it. An AI agent can prepare it; only an analyst decides whether the match is true.

What is the template for?

Sanctions screening compares names and details against official lists. Fuzzy matching is deliberately generous, so most hits are not the listed person. Each one still has to be examined, and the examination has to be recorded so it can be shown later why a hit was discounted or confirmed.

The explanation is the record of the examination, prepared in a fixed shape. It answers "why did this match?" rather than "is this the person?". That distinction is the whole point: sanctions hits: explaining a match, not clearing it argues that the agent's job is the first question, and the analyst's the second.

When should it be used?

For every screening hit that is not automatically resolved by an exact identifier match on file, at onboarding, on list updates, and on payments. For payments, time matters, because the payment is held until the hit is resolved; the workflow is in how to review payment sanctions hits before release with an AI agent. Whether an AI agent can do sanctions screening covers the wider question.

How do you complete each section?

Hit details. List, entry reference, match score, matched field.

Field comparison. One row per identifier: name, aliases, date of birth, nationality, address, identification numbers, and for entities, registration details. For each: the list value, your value, whether they agree, differ or one is missing, and how distinctive the identifier is. A full date of birth that differs is strong evidence; a common surname that matches is weak.

Agent summary. A neutral statement of what agrees and what differs. The agent must not use the words "false positive" or "cleared". Those are the analyst's conclusions.

Further information. What would resolve doubt, such as a passport number or a date of birth, and whether it has been requested.

Analyst decision. True match, not a match, or unresolved pending information, with reasons.

The sanctions match explanation template

Part 1: hit details

Part 2: field comparison

Part 3: agent summary. What agrees; what differs; what is missing.

Part 4: further information requested.

Part 5: analyst decision. Outcome; reasons; analyst; date; second review if required.

A filled sample

A hypothetical bank's onboarding screen flags a new customer.

Agent summary: Name partially matches a common name. Date of birth year on the list is several decades before the customer's verified date of birth. Nationality differs. No identifier on the list matches an identifier on file.

Analyst decision: Not a match. The verified date of birth puts the customer decades younger than the listed person, and nationality differs. Recorded with reasons.

The agent's summary stayed neutral even though the outcome looked obvious. That matters for the cases where it is not obvious: an agent in the habit of calling hits "false positives" will one day call a true match one.

What should the customer be told?

Usually nothing about the screening itself, and never anything that could alert a listed person. Where a customer asks why a decision about them was made by automated means, your data protection obligations may apply; see GDPR right to explanation and AI agents: what to tell people, and take advice before disclosing anything about screening. The boundary for compliance agents is in AI compliance agents: second line, never the last word.