LO
Loan officer / credit 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
● 1 verified event for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
A high curve that mostly predates the period it is drawn over, and reading it correctly requires that: statistical credit scoring arrived in the 1960s and had already automated the core calculation long before this chart begins, so the start is high for historical reasons. What 2023 onward adds is document gathering and verification — a rules-based workflow with a checkable outcome, extended by open banking feeds. It flattens at the exception and the signature, because an exception is by definition a case the model scored wrong. The precedent is the valuable part: sixty years of automating the central task did not remove the occupation, it changed who gets in and how many.
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