How do I set up a document processing agent for claims intake with human review and structured data capture?

Here’s a practical way to set up a document processing agent for claims intake that combines OCR/document understanding, structured extraction, and human review.

1) Define the intake workflow

A common claims flow looks like this:

1. Receive documents

   • Email attachments
   • Portal uploads
   • Scanned paper/PDFs
   • Photos of forms or damage

2. Classify the document

   • Claim form
   • ID card
   • Medical bill
   • Police report
   • Repair estimate
   • Supporting evidence

3. Extract structured fields

   • Claimant name
   • Policy number
   • Date of loss
   • Incident type
   • Provider/vendor
   • Amounts, diagnosis/procedure codes, etc.

4. Validate

   • Required fields present?
   • Values in expected formats?
   • Cross-document consistency?

5. Route for human review

   • Low-confidence extractions
   • Missing critical fields
   • Conflicting values
   • Fraud/red-flag cases

6. Commit to downstream systems

   • Claims system / CRM / case management
   • Audit log
   • Notifications

---

2) Use a 3-layer architecture

A strong design is:

A. Ingestion layer

Handles file intake and normalization.

• Accept PDF, TIFF, JPG, PNG, DOCX
• Convert pages to images if needed
• Split multi-page files
• Store originals in object storage

B. Extraction/agent layer

This is the “document processing agent.”

• OCR + layout detection
• Document classification
• Field extraction into a schema
• Confidence scoring
• Reasoning over multiple documents in the claim packet

C. Review/orchestration layer

• Human-in-the-loop UI
• Exception queues
• Audit trail
• Final approval and export

---

3) Define the data schema first

Before building extraction, define exactly what you want to capture.

Example claims intake schema:

{
  "claim_id": "",
  "claimant": {
    "full_name": "",
    "date_of_birth": "",
    "address": "",
    "phone": "",
    "email": ""
  },
  "policy": {
    "policy_number": "",
    "carrier": "",
    "coverage_type": ""
  },
  "incident": {
    "date_of_loss": "",
    "incident_type": "",
    "location": "",
    "description": ""
  },
  "documents": [
    {
      "type": "",
      "filename": "",
      "page_count": 0
    }
  ],
  "financials": {
    "billed_amount": 0,
    "paid_amount": 0,
    "currency": ""
  },
  "extraction_meta": {
    "confidence": 0,
    "needs_review": false,
    "issues": []
  }
}

Good practice:

• Mark fields as required / optional
• Add validation rules
• Track source document and page number for each extracted field
• Keep confidence scores per field, not just per document

---

4) Choose extraction methods

Typically you’ll combine several methods:

OCR

For text extraction from scans/images.

• AWS Textract
• Azure Document Intelligence
• Google Document AI
• Tesseract for open-source baseline

Document classification

Determine what type of document each file is.

• Rules based on keywords
• ML classifier
• LLM-based classification using page text

Structured extraction

Use either:

• Template-based extraction for standardized forms
• ML/doc AI for semi-structured docs
• LLM extraction for flexible forms and narrative docs

A useful pattern:

• OCR first
• Then ask the agent to map text to your JSON schema
• Require citations to page/line/snippet if possible

---

5) Add human review where it matters

Don’t send everything to a human. Route only exceptions.

Common review triggers

• Confidence below threshold
• Missing mandatory fields
• Multiple possible values
• Handwritten sections unreadable
• Unusual amounts or dates
• Policy number format invalid
• Incident date after claim date

Review UX

Human reviewers should see:

• The document viewer
• Highlighted extracted fields
• Confidence scores
• Suggested values
• Editable structured form
• Source snippet for each field

Human actions

• Approve
• Correct
• Reject
• Escalate
• Mark as fraud/suspect
• Add notes

Those corrections should feed back into your system for improvement.

---

6) Implement orchestration logic

A simple control flow:

1. Upload document
2. Run OCR
3. Classify document type
4. Extract fields
5. Validate against rules
6. If confidence high and validation passes:
   • Auto-accept
7. Else:
   • Send to review queue
8. After review:
   • Persist final structured claim record
   • Log all changes

Pseudo-logic:

result = process_document(file)

if result.confidence >= 0.9 and not result.validation_errors:
    status = "auto_approved"
else:
    status = "needs_human_review"
    send_to_review_queue(result)

save_claim(result, status)

---

7) Use a robust validation layer

This is essential for claims intake.

Examples

• Policy number matches expected format
• DOB not in the future
• Date of loss not after submission date
• Currency consistent
• Amounts non-negative
• Provider NPI/license valid if applicable
• Claimant name matches policyholder records

Validation can also compare across documents:

• Claim form says accident date = Jan 3
• Police report says Jan 4
• Flag discrepancy

---

8) Build for traceability and audit

Claims workflows usually need auditability.

Store:

• Original documents
• OCR output
• Extracted JSON
• Confidence per field
• Reviewer edits
• Timestamps
• Model/version used
• Decision rationale

This helps with:

• Compliance
• Appeals
• QA
• Model debugging

---

9) Improve the agent with feedback loops

Each human correction is training data.

Track:

• Field-level error rates
• Document-type error rates
• Common validation failures
• Reviewer override frequency
• Time to resolution

Use this to:

• Update prompts
• Improve templates
• Fine-tune classifiers
• Adjust review thresholds

---

10) Recommended implementation stack

A typical modern stack:

Backend

• Python / FastAPI
• Queue: Celery, SQS, RabbitMQ, Kafka
• Storage: S3 / Blob Storage
• Database: Postgres

Document AI / OCR

• AWS Textract, Azure Document Intelligence, or Google Document AI
• Optional fallback OCR: Tesseract

LLM layer

• For flexible extraction, summarization, normalization
• Use structured output / JSON schema enforcement if available

Review app

• React frontend
• PDF viewer with field highlighting
• Reviewer queue and annotations

Observability

• Logs, metrics, tracing
• Extraction confidence dashboards
• QA sampling

---

11) Practical safeguards

For claims processing, include:

• PII encryption at rest and in transit
• Role-based access control
• Redaction for non-privileged users
• Prompt/output filtering for sensitive data
• Rate limiting and retry handling
• Deterministic schema validation
• Versioned prompts and models

---

12) Suggested MVP plan

If you want to get started quickly:

Phase 1: MVP

• Upload documents
• OCR
• Extract 10–20 key fields
• Human review queue
• Export JSON/CSV

Phase 2: Scale

• Add document classification
• Add confidence scoring
• Add cross-document reconciliation
• Add reviewer analytics

Phase 3: Automation

• Auto-route low-risk claims
• Fraud/anomaly flags
• Continuous learning from reviewer corrections

---

13) Example field set for claims intake

For a standard intake packet, start with:

• Claim number
• Claimant full name
• Policy number
• Date of loss
• Incident type
• Contact info
• Address
• Provider/vendor name
• Invoice amount
• Coverage type
• Document type
• Source page
• Confidence score

---

If you want, I can also provide:

1. a reference architecture diagram,
2. a sample JSON schema for claims intake, or
3. a Python/FastAPI skeleton for the agent and review queue.