Six Questions an MLRO Can Ask Before Signing Off Alert Closures

AML Closure Review in the UK and Europe: Questions That Test the Decision

A closure rate tells you how many alerts left the queue. It does not tell you whether they left for good reasons. For an MLRO, that gap matters, because every closure is a decision someone may later ask you to explain.

In short: sample a few closures each month and ask six questions about what the analyst could see, what they considered and what they could not reach. The answers show where the process is thin.

Why the review matters now

The UKFIU's latest annual report counts 896,414 SARs received between April 2025 and March 2026, up 3% on the year before. Closure decisions sit upstream of that stream: each one is a choice to report or not to report. Quality at the closure stage shapes what reaches the NCA, and what does not.

The six questions

  1. What could the analyst see when the alert opened? Not what they found later. What was on the screen.
  2. Was an earlier case on this customer or a linked account considered? If the file is silent, was it searched for, or just not found?
  3. Is the "consistent with profile" statement dated and sourced? A profile confirmed three years ago is a weak anchor.
  4. What was the strongest argument against closing? If the file does not say, the decision was not tested.
  5. Who else looked, and did they see the same information? Two reviewers with different views of the customer are not a second opinion.
  6. What did the analyst not have access to? A recorded gap can be fixed. An unrecorded one repeats.

How to use them without creating a monster

Sample five closures a month. Log the answers in a simple sheet. Do not score individuals. Expect the "not available" answers to cluster around the same few gaps, and treat that cluster as a systems backlog, not a training issue. The context gap behind those answers is covered in more detail on our blog.

Conclusion

The six questions take minutes to ask and show whether closures rest on evidence or habit. TechnoXander builds investigation tooling around making that evidence visible to the analyst and the reviewer alike.

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