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Hallucination Risk in Automated Due Diligence Summaries
AI summaries should shorten review time without inventing facts or hiding weak evidence.
Why it matters
AI summaries are attractive because they turn messy documents into readable paragraphs. The risk is that a fluent summary can sound more certain than the evidence allows. In supplier verification, a hallucinated relationship, date, license status, or risk conclusion can change a payment decision.
Evidence to collect
Every summary should link back to source fields. If the summary says the beneficiary matches the invoice issuer, the reviewer should see both fields. If it says a certificate supports a product, the certificate scope and product model should be visible.
How to review it
Use summary outputs as drafts. Require citations or field references for factual claims. Separate facts found in documents from model interpretation. If the source is missing, the summary should say that evidence is unavailable rather than filling the gap.
Where buyers get misled
Teams get misled when summaries remove uncertainty. A model may connect companies, translate scope language, or infer product coverage without adequate support. The better design is to make uncertainty visible.
Practical next step
Create a summary rule: no factual claim without a source pointer. This keeps the output useful while making the final decision auditable.
Working checklist
- Require source pointers.
- Label interpretation separately.
- Keep uncertainty visible.
- Review high-risk claims manually.
- Reject summaries that cannot show evidence.