How can a buyer check the quality of generated approval data?

Quality in this workflow can be checked mostly by recomputation, which makes it easier to inspect than free-text data.

Start with authority consistency. For each approval event, a short script should be able to look up the approver's role, the delegation in force on that date, the limit for the category and risk class, and the policy version in effect. Events labelled valid should pass; events labelled invalid should fail for the stated reason code. Disagreements point to label errors.

Next, check referential integrity: every event points to an existing request, role and grant, and dates are ordered sensibly, with no approval before submission and no delegation used after revocation.

Then look at distribution. Are there requests close to limits, delegations that lapse mid-case, rejected and withdrawn items, and second-approver rules that trigger? A set where every case is a clean approval teaches the wrong lesson.

Ask someone who has worked in approvals to read a handful of cases and notes. They will spot implausible escalations, unnatural wording or limits that no business would set.

Finally, review grounding and disclosure risk. The output is generated, not real records, and generation does not by itself guarantee anything about privacy or similarity to a source. Check samples for text or structures that look too specific to be invented, and ask how the generation process addresses that.