LO
Compliance officer — how we know
The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Which technologies matter here#
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
How it got here#
The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
● 2 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
The rise is the alert queue, which is the same task as a security analyst's and a guard's: very low true-positive rate, human vigilance collapsing within about twenty minutes, and correlation that a machine can check. Financial crime monitoring is among the most heavily automated alert pipelines anywhere for exactly that reason, and evidence logging follows the same path. It flattens because reading a rule is only augmented — the hard half is knowing which of this company's processes a clause lands on — and because refusing the business is held in place by organisational structure rather than by difficulty. The curve cannot see the thing that most decides this job: who the function reports to.
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
Method and sources#
- Assessment date
- 2026-09-14
- Basis of the task judgements
- 2 evidence-backed · 2 platform inference · 0 not enough evidence
- Verified events
- 2