Your adjusters spend 40% of their day re-keying data from PDFs into your claims system

The Data Entry Burden

Studies show that claims adjusters spend 35-45% of their workday on data handling activities rather than actual claims decisions. Here's what that looks like in practice:

• ACORD forms: Open PDF, zoom in on tiny text, manually type policy numbers, coverage limits, deductibles, loss descriptions into claims system fields. 8-12 minutes per form.
• Medical records: Extract patient name, DOB, diagnosis codes, procedure codes, treatment dates, provider information. 15-20 minutes per multi-page record.
• Repair estimates: Line-item entry of parts, labor, tax, totals. Compare to prior estimates. 10-15 minutes per estimate.
• Invoices and bills: Vendor name, invoice number, date, line items, amounts, payment terms. 5-8 minutes per invoice.
• Police reports: Incident details, parties involved, officer information, narrative summary. 12-18 minutes per report.

For an adjuster handling 20 claims per day with an average of 3 documents per claim, that's 3-4 hours per day spent on data entry.

The Compounding Costs

Manual data entry doesn't just waste time — it creates cascading problems:

• Transcription errors: Industry studies show 3-5% error rates on manually entered data. Typos in policy numbers, wrong claim amounts, transposed digits in VINs.
• Incomplete data: Adjusters skip fields when they're in a hurry. Missing information causes downstream delays when underwriting needs complete records for settlement authority.
• Inconsistent formatting: One adjuster enters dates as "02/14/2024", another as "Feb 14 2024", another as "2024-02-14". Reporting and analytics become impossible.
• Delayed decisions: Claims sit in "pending data entry" queues for hours or days. By the time the data is entered, it may already be out of date.
• Adjuster burnout: Talented professionals who went into insurance to help people spend their days as glorified typists. Turnover follows.

1. Document-Specific Extraction Models

Regure maintains separate AI models for each major document type:

• ACORD Forms: Trained on all major ACORD versions (25, 27, 28, 30, 35, 101, 126, 130, 140). Extracts 50+ fields per form including policy info, coverage limits, deductibles, insured parties, loss details.
• Medical Records: Extracts patient demographics, diagnosis codes (ICD-10), procedure codes (CPT), treatment dates, provider information, prescription details, and clinical notes.
• Repair Estimates: Line-item extraction of parts, labor hours, labor rates, tax, total. Identifies estimating system (CCC, Mitchell, Audatex) and version.
• Invoices & Bills: Vendor information, invoice numbers, dates, line items with descriptions and amounts, payment terms, totals.
• Police Reports: Incident details, location, date/time, parties involved, officer names/badges, narrative summaries, diagram extraction.
• Legal Documents: Demand letters, releases, subrogation notices, court filings — extract key dates, amounts, parties, terms.

Each model understands document layout variations and can handle both native PDFs and scanned images. See Document Processing for the full list of supported document types.

2. Field-Level Validation

Extracted data isn't just dumped into your system. Regure validates every field against business rules and claims context:

• Format validation: Policy numbers match expected patterns. Dates are valid. VINs pass check-digit validation. Phone numbers are properly formatted.
• Range validation: Claim amounts are within expected ranges for loss type. Dates are logical (loss date before claim date, treatment dates after incident date).
• Cross-document validation: Policy number extracted from ACORD form matches policy number from declaration page. Claimant name is consistent across documents.
• Historical validation: New claim amount is flagged if it differs significantly from initial estimate. Provider information matches known vendor database.

Fields that fail validation are flagged for human review before being pushed to the claims system. This prevents garbage-in-garbage-out problems.

3. Confidence Scoring

Every extracted field includes a confidence score (0-100%). High-confidence extractions (95%+) are automatically pushed to workflows. Lower-confidence extractions are queued for adjuster review.

Example confidence scores:

• Policy Number: 99% (clear, standard format)
• Loss Date: 97% (unambiguous date field)
• Claim Amount: 88% (handwritten, requires review)
• Claimant Name: 92% (slight OCR uncertainty on middle initial)

Adjusters see exactly which fields need attention and why. They don't waste time reviewing perfect extractions — only the edge cases.

4. Structured Data Output

Extracted data is output in structured JSON format that maps directly to claims system fields:

{
  "document_type": "ACORD_25",
  "policy_number": "CA-8392-047",
  "insured_name": "ABC Manufacturing Inc",
  "loss_date": "2024-02-14",
  "loss_description": "Water damage to warehouse",
  "coverage_type": "Property",
  "claim_amount": "$47,250.00",
  "confidence_scores": {
    "policy_number": 0.99,
    "loss_date": 0.97,
    "claim_amount": 0.94
  }
}

This structured output integrates directly with Guidewire, Duck Creek, Sapiens, and other core systems via RESTful APIs. No manual data mapping required.

5. Human-in-the-Loop Review

For fields below confidence thresholds or flagged by validation rules, Regure presents a side-by-side review interface:

• Original document on the left
• Extracted fields on the right with confidence scores
• Flagged fields highlighted for attention
• One-click corrections if extraction is wrong
• Approve button to push validated data to claims system

Review time averages 45 seconds per document vs 8-15 minutes for full manual entry. Adjusters focus on validation, not transcription.

6. Continuous Learning

When adjusters correct extractions, Regure learns from those corrections. The AI models retrain nightly on validated data, improving accuracy over time.

Example improvement curve for a typical implementation:

• Month 1: 92% accuracy, 15% requiring review
• Month 3: 95% accuracy, 8% requiring review
• Month 6: 97% accuracy, 4% requiring review
• Month 12: 98.5% accuracy, 2% requiring review

The system gets better the more you use it. Models adapt to your specific document variations and business rules.