Not all reported numbers deserve equal confidence
A figure from an identified financial statement with a defined reporting period carries different weight from an undated online claim. The number may be the same, but the ability to verify and interpret it is not.
A useful system distinguishes primary documents, reliable third-party reporting, analytical estimates and unverified statements. It retains the original source rather than only the extracted value.
Freshness changes the question
Older financials may still be useful for trends, but they cannot automatically be treated as a current operating picture. A model should show the data period and explain when assumptions extend beyond the available statements.
Where a reporting period is missing, the interface should say so instead of silently filling the gap with fabricated continuity.
Make uncertainty navigable
Evidence completeness can be explored through a checklist: audited accounts where available, capital structure, debt disclosures, ownership rights, corporate announcements and material events. Each missing item becomes a research question.
In a source-grounded AI system, every generated observation should link back to the passage that supports it. Contradictions and unavailable evidence should be shown, not erased.
Questions worth asking.
- When was the evidence recorded?
- Can the financial statement be traced?
- What is unknown?
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