Automating claims assessment
Claims assessment in Insurance. Liability-gated, 50% repeatability, Burns-tier risk. Here is what the framework says about the shape of this work, and what supervision it would need if you ran it.
What the shape of this task tells you
This task is Burns-tier. The audit blocks the cost-optimised scenarios outright here and shows no savings figure at all, regardless of how the arithmetic looks, because a good number would be misleading rather than useful.
Repeatability 50%. A substantial share of instances deviate from the standard path. That is the part that decides this verdict: the exceptions are the work, and they are what a pilot measured on routine volume will not show you.
Judgment high, risk Burns. Together these set both the readiness penalty and the supervision this work needs. Errors mean legal, financial or safety damage that is hard to reverse. No cost argument outranks that.
The failure mode to test for
A claim declined on a reading of the policy that would not survive challenge
This is the one to build your sample around. Errors that survive review are the ones that look correct, so a review pass that only inspects items flagged as unusual will systematically miss it.
The oversight contract
Delegation level 1 of 4 — Suggest only. Read-only. No write access to production.
No sampling schedule is published for a Burns-tier task, because the recommendation is not to run it unsupervised at any rate. Delegation stays at level 1 — the model suggests, a person owns every output.
Liability-gated
This verdict rests on who is accountable when the work is wrong. A more capable model does not change who answers for it, so this one is structural and expected to hold.
Get the full verdict for your numbers
The score above is the shape of the work. Enter headcount, salary, hours and output and the audit returns the readiness score, three costed scenarios and a supervision schedule sized to your volume.
Audit this taskCommon questions
Should claims assessment be automated?
It depends on your volume as well as the shape of the work. On shape alone this task scores between 0 and 0 out of 100, with 50% repeatability, high judgment and Burns-tier risk. The remaining 45 points of the score come from your cost per unit and whether your manual spend covers the fixed costs, so a low-volume version of this task can score well below a high-volume one.
How much output would need to be reviewed?
At steady state, about 100% of output, after a ramp starting at 100% for the first fortnight. The error ceiling is 0% — above that, automation pauses.
What is the most likely way this goes wrong?
A claim declined on a reading of the policy that would not survive challenge.
Will a better model change this verdict?
This verdict rests on who is accountable when the work is wrong. A more capable model does not change who answers for it, so this one is structural and expected to hold.