Claims in Hours, Not Weeks: Evidence-Backed Extraction for Insurance Adjusters

Claims operations are document operations.

Even in modern carriers, a single claim can involve:

• FNOL forms
• police or incident reports
• repair estimates and invoices
• medical bills (for certain lines)
• policy documents and endorsements
• correspondence

Adjusters and examiners spend a lot of time doing two things:

1. extracting facts,
2. proving those facts came from the file.

The second part is usually the slowest.

Citation-backed extraction changes the workflow: every extracted value includes proof (page + highlighted region + snippet), so verification becomes part of the UI—not a manual “open PDF and scroll” ritual.

High-value claim workflows where citations pay off

1) FNOL triage

Extract:

• claimant name
• policy number
• date of loss
• location
• loss type
• reported damages/items

Why citations matter:

• FNOL forms are often inconsistent, incomplete, or duplicated.
• Evidence helps confirm what was actually reported, and what was inferred.

2) Estimate and invoice review

Extract:

• line items
• labor/material totals
• taxes and fees
• repair timelines
• vendor identifiers

Then compare: estimate vs invoice vs coverage rules.

Citations let the adjuster jump directly to the line item that triggered a discrepancy.

3) Coverage confirmation (policy + endorsements)

Extract:

• coverage limits
• deductibles
• exclusions
• effective dates

Citations are critical here: reviewers need the exact clause, not a paraphrase.

4) Subrogation and recovery signals

Extract:

• third-party details
• incident descriptions
• indicators of liability

Evidence helps route claims to subrogation teams quickly and defensibly.

The document-pack approach (the only one that works in claims)

Claims aren’t single PDFs. They’re packets.

A durable design is:

• ingest the entire packet,
• extract the same canonical fields from each document,
• run consistency checks,
• and surface conflicts with evidence side-by-side.

Example: “date of loss” appears in three places. If two disagree, your system should:

• flag it,
• show both citations,
• and let the reviewer choose the authoritative source.

That’s how you reduce downstream rework.

A review UI that adjusters adopt

If you want adoption, design for the adjuster’s real job:

• resolve uncertainty quickly
• document the decision
• move to the next file

A practical UI pattern:

• left panel: extracted fields + confidence + status
• right panel: PDF viewer with citation highlighting
• hotkeys: verify / correct / escalate / request more info

Adjusters don’t want “AI answers.” They want faster file review with a clean record.

Fraud and discrepancy detection (without overpromising)

You don’t need “fraud prediction” to create value. You need discrepancy detection that’s easy to validate.

Examples that citations make easier:

• invoice total doesn’t equal sum of line items
• duplicate invoice numbers across claims
• vendor address mismatch
• inconsistent dates across documents

Because every flag is tied to evidence, reviewers can confirm quickly and avoid noisy alerts.

Evidence-backed extraction also improves customer experience

When a claim is delayed, customers ask:

• “What’s missing?”
• “Why was this denied?”
• “Where is the deductible stated?”

Evidence-backed workflows make communication easier:

• you can reference specific document sections internally,
• and produce cleaner explanations externally (without leaking sensitive text).

What to measure

Claims transformation metrics that typically move first:

• cycle time (intake → first decision)
• touches per claim
• time-to-verify key fields
• rework rate due to missing/conflicting information

If your system reduces time-to-verify, cycle time usually follows.

Claims teams don’t need magic. They need tools that help them prove decisions quickly.

Citations turn extraction into evidence—and evidence into speed.