What does an institution need in place before deploying AI transaction monitoring?

Detailed Explanation

Clean data inputs first. Models amplify whatever the source systems contain, so inconsistent customer records, missing counterparty identifiers or unreconciled account hierarchies produce confident nonsense. Then validation metrics agreed before go-live — what counts as an improvement, measured against what baseline, over what window. Deciding this afterwards makes every result unfalsifiable. Then the governance wrapper: documented model purpose, periodic validation, bias testing, an escalation path, and a change log. The technical build is usually the shorter half of the work.