Consider a proposed workflow that reads financial statements and fills a structured review worksheet. The demonstration looks convincing, but approval depends on a different test: whether each required value is correct, belongs to the intended reporting period and can be traced to the authorized document. Keep financial advice and autonomous posting outside this acceptance decision.
The practical approval standard should be field-level correctness, traceability and manageable exception work, not a polished summary or an aggregate accuracy claim. Approve a defined document population and review process, rather than granting blanket approval to an extraction tool.
Define the extraction contract before testing
Start with the information finance actually needs. In an Intelligence Squared discussion sponsored by Box, an adviser workflow was narrowed from preparing a report to first extracting information from financial and stock statements into a usable structure. That distinction is useful here: extracting evidence is a bounded task, separate from deciding what the evidence means for a financial recommendation.
Write an extraction contract describing eligible documents, required fields and the intended downstream worksheet. For each field, specify its meaning, data type, permitted normalization and criticality. Finance should classify fields by the consequence of error. Treat entity, reporting period, currency and scale as critical wherever a mistake would change the interpretation of an amount.
Define correctness as more than matching visible digits. Require the amount to retain its row label, column heading, period, currency, unit and sign. Preserve the source text alongside any normalized value. A value copied faithfully from the wrong column should fail the test, even if that value appears elsewhere in the document.
The same discussion emphasizes reaching a usable quality level, with directly responsible people and clear exit criteria. Translate that into named responsibilities: a finance owner defines meaning, an automation owner controls extraction changes, and a risk reviewer approves the proposed controls. Agree the acceptance gates before examining results, so a successful-looking demonstration cannot quietly redefine success.
Build a document set with independently checked answers
The acceptance pack should begin with an authorized document register. Record each document’s identity, entity, reporting period, version, inclusion reason and processing authorization. Select examples across the document types, layouts, languages and scan conditions the proposed workflow will actually accept. Keep a separate challenge set for difficult cases rather than allowing those cases to disappear inside an overall score.
Reference values provide a concrete comparison method. Ramp’s financial-document benchmark compares extracted metrics with transcriptions made by its risk operators. For your acceptance pack, go further operationally: have finance prepare expected values and have another qualified reviewer check them independently, resolving disagreements before scoring the extraction.
Store an evidence record for every expected field: document version, page, table or section, row and column context, source text, expected normalized value and criticality. Explicitly label fields that are absent, unreadable or ambiguous. Otherwise, the test cannot distinguish a legitimate blank from a missed extraction.
Use a coverage checklist to make the difficult cases inspectable:
- Tables: test merged headings, continuation pages, subtotals and adjacent reporting columns.
- Dates: distinguish period end, period covered, publication date and any comparative period.
- Currencies and units: retain the applicable currency and whether amounts use a stated scale.
- Versions: include superseded and revised documents, with an explicit rule for selecting the authorized version.
Reserve documents that developers have not used for tuning, and record the extractor, prompt, schema and configuration used for each run. If a failed case leads to a change, retain it as a regression test and assess the changed configuration against the reserved set.
Score field errors separately and test abstention
Make the comparison rules explicit before calculating metrics. Decide which formatting differences are acceptable, which normalizations are allowed and whether any field-specific tolerance is justified. Do not let approximate matching turn a wrong period, currency or unit into an accepted answer.
| Outcome | Acceptance-test meaning | Required response |
|---|---|---|
| Missing | A required, available value was not returned. | Record the omission and route for completion. |
| Incorrect | A returned value or its required context differs from the checked answer. | Record the mismatch and correction. |
| Unsupported | The returned value has no valid supporting source passage in the authorized document. | Block acceptance and investigate the evidence failure. |
| Abstained | The extractor explicitly declines to provide a value. | Check whether abstention was appropriate and route the case. |
For this worksheet, define field precision as correct returned field instances divided by all returned field instances, and field recall as correct returned field instances divided by all required, available field instances. Specify how duplicates and repeated table rows are matched. Report missing, incorrect and unsupported counts separately, with critical-field results and results by document category visible alongside the totals.
Also report documents whose required fields all pass. A strong aggregate result should not override a failed critical-field gate. Keep abstentions visible in coverage and workload reporting: declining an ambiguous value may be the right behavior, but finance still needs someone to resolve it.
If confidence scores are available, evaluate the proposed routing threshold against checked outcomes. Do not treat a high score as evidence that a field is correct. Define abstention triggers for missing source references, conflicting versions, unclear units and unresolved table context. Then test that those triggers actually prevent an unverified value from reaching the accepted worksheet.
Sign off the review workflow, not just the extractor
Run the acceptance exercise through the intended reviewer interface. Require reviewers to open the cited source, correct or reject a proposal, record a reason and send unresolved cases to an accountable owner. Include an escalation route for disputed reference values and document-version conflicts. The test ends with an accepted or explicitly unresolved record, not merely a completed extraction request.
Measure active review time, correction time, waiting time and unresolved backlog by exception category. The established analysis in testing an AI workflow before funding an ERP upgrade likewise recommends measuring accepted results, incorrect proposals, exceptions, overrides and review effort while keeping the denominator visible. Here, apply that principle to extraction alone; no ERP upgrade is required to make this acceptance decision.
Use a reusable sign-off sheet containing: approved scope and exclusions; document-set version; checked reference values; critical-field classifications; matching rules; quality results; source-reference checks; abstention results; exception owners; measured reviewer effort; and unresolved defects. Add organization-set thresholds, the decision owner and retest triggers. Record whether approval means limited use with mandatory review, broader use under specified controls, or no deployment.
For the financial decision, carry actual review and exception effort into the cost per completed workflow, rather than comparing extraction charges alone. Before approval, ask finance and risk to sign the same acceptance sheet against the same frozen test run. If you need help defining the extraction pipeline or its validation workflow, discuss those requirements with Digiventi’s Data & AI specialists.
