How can a buyer inspect the quality of generated allocation data?

Quality in generated allocation data can be inspected with checks a team can run itself, before any model is trained.

**Arithmetic and reconciliation.** For every case, the allocated amounts plus any suspense amount should equal the receipt. Balances after allocation should equal balances before, minus what was applied.

**Referential integrity.** Every receipt, obligation, policy and allocation identifier should resolve. Reversals should point to an allocation that existed earlier in time.

**Policy consistency.** Re-derive the expected outcome from the stated rule with an independent script and compare it with the label. Disagreements point to label errors or ambiguous rules.

**Difficulty spread.** Tag cases by exception type and count them. A set made mostly of exact-reference matches will flatter a weak model.

**Text realism.** Read a sample of payer references, notes and queries. Look for repeated phrasing, uniform tone and templates that a model could memorise.

**Grounding and disclosure.** Ask how the generated cases relate to observed practice, and review a sample for anything that looks like a real party or record. Generated does not mean free of risk, so inspect it.

**Split hygiene.** Check that near-duplicate cases do not straddle train and test splits.

Record what you find in a short review log. It makes the second round of a pilot far more specific than a general comment on quality.