# Deepread Insurance > Extract structured data from insurance claims, policies, EOBs, and loss reports. Pre-built schemas for claimant info, coverage, damages, adjuster notes. PII redaction for compliant sharing. 97%+ accuracy. Free 2,000 pages/month. - Skill: `deepread-tech/deepread-insurance` (Agent Skill) - Install (CLI): `npx skillmds@latest add deepread-tech/deepread-insurance` - Raw SKILL.md: https://api.skillmd.com/api/skills/deepread-tech/deepread-insurance/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: deepread-tech (https://skillmd.com/u/deepread-tech) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/deepread-tech/deepread-insurance --- # DeepRead Insurance Document Processing Extract structured data from insurance claims, policy documents, Explanations of Benefits (EOBs), loss reports, and adjuster notes. Then redact claimant PII before sharing with third parties. > This skill instructs the agent to POST documents to `https://api.deepread.tech` and poll for results. No system files are modified. ## What You Get Back Submit a claim form and get structured JSON. Extracted fields come back as a list under `extraction.fields[]` (each field has `key`, `value`, `needs_review`, and `location.page`): ```json { "schema_version": "dp02", "status": "completed", "extraction": { "fields": [ {"key": "claim_number", "value": "CLM-2026-078432", "needs_review": false, "location": {"page": 1}}, {"key": "policy_number", "value": "POL-HO3-445521", "needs_review": false, "location": {"page": 1}}, {"key": "claimant_name", "value": "Robert Johnson", "needs_review": false, "location": {"page": 1}}, {"key": "date_of_loss", "value": "2026-02-14", "needs_review": false, "location": {"page": 1}}, {"key": "date_reported", "value": "2026-02-15", "needs_review": false, "location": {"page": 1}}, {"key": "loss_type", "value": "Water Damage", "needs_review": false, "location": {"page": 1}}, {"key": "loss_description", "value": "Burst pipe in basement caused flooding to finished living area, damaged drywall, carpet, and personal property", "needs_review": false, "location": {"page": 2}}, {"key": "property_address", "value": "789 Elm Dr, Denver, CO 80202", "needs_review": false, "location": {"page": 1}}, {"key": "coverage_type", "value": "Homeowners HO-3", "needs_review": false, "location": {"page": 1}}, {"key": "deductible", "value": 1000.00, "needs_review": false, "location": {"page": 1}}, {"key": "estimated_damages", "value": 24500.00, "needs_review": true, "review_reason": "Multiple estimates on different pages", "location": {"page": 1}}, {"key": "adjuster", "value": "Sarah Martinez, License #ADJ-44521", "needs_review": false, "location": {"page": 3}}, {"key": "line_items", "value": [ {"category": "Structural", "description": "Drywall replacement — basement", "amount": 8500.00}, {"category": "Flooring", "description": "Carpet removal and replacement", "amount": 6200.00}, {"category": "Personal Property", "description": "Damaged furniture and electronics", "amount": 5800.00}, {"category": "Mitigation", "description": "Water extraction and drying", "amount": 4000.00} ], "needs_review": false, "location": {"page": 3}}, {"key": "status", "value": "Under Review", "needs_review": false, "location": {"page": 1}} ] } } ``` Fields with `needs_review: true` need human review (check `review_reason`). Everything else is high-confidence. ## Setup ### Get Your API Key ```bash open "https://www.deepread.tech/dashboard/?utm_source=clawhub" ``` Save it: ```bash export DEEPREAD_API_KEY="sk_live_your_key_here" ``` ## Insurance Claim Schema Pre-built schema for insurance claims and loss reports: ```json { "type": "object", "properties": { "claim_number": {"type": "string", "description": "Claim number or reference ID"}, "policy_number": {"type": "string", "description": "Insurance policy number"}, "claimant_name": {"type": "string", "description": "Name of the claimant or insured"}, "date_of_loss": {"type": "string", "description": "Date the loss or incident occurred (YYYY-MM-DD)"}, "date_reported": {"type": "string", "description": "Date the claim was reported (YYYY-MM-DD)"}, "loss_type": {"type": "string", "description": "Type of loss (Water Damage, Fire, Theft, Auto Collision, Liability, etc.)"}, "loss_description": {"type": "string", "description": "Detailed description of the loss or incident"}, "property_address": {"type": "string", "description": "Address of the property or location of incident"}, "coverage_type": {"type": "string", "description": "Type of coverage (Homeowners, Auto, Commercial, Liability, etc.)"}, "deductible": {"type": "number", "description": "Policy deductible amount"}, "estimated_damages": {"type": "number", "description": "Total estimated damage amount"}, "adjuster": {"type": "string", "description": "Claims adjuster name and license number"}, "line_items": { "type": "array", "items": { "type": "object", "properties": { "category": {"type": "string", "description": "Damage category (Structural, Personal Property, Medical, etc.)"}, "description": {"type": "string", "description": "Description of damage or expense"}, "amount": {"type": "number", "description": "Cost or estimate amount"} } }, "description": "Itemized list of damages or expenses" }, "status": {"type": "string", "description": "Claim status (Filed, Under Review, Approved, Denied, Settled)"}, "settlement_amount": {"type": "number", "description": "Final settlement amount if resolved"}, "denial_reason": {"type": "string", "description": "Reason for denial if applicable"} } } ``` ## Extract Data From Insurance Documents ### Python ```python import requests import json import time API_KEY = "sk_live_YOUR_KEY" BASE = "https://api.deepread.tech" headers = {"X-API-Key": API_KEY} schema = json.dumps({ "type": "object", "properties": { "claim_number": {"type": "string", "description": "Claim number"}, "policy_number": {"type": "string", "description": "Policy number"}, "claimant_name": {"type": "string", "description": "Claimant name"}, "date_of_loss": {"type": "string", "description": "Date of loss (YYYY-MM-DD)"}, "loss_type": {"type": "string", "description": "Type of loss"}, "loss_description": {"type": "string", "description": "Description of the loss"}, "coverage_type": {"type": "string", "description": "Coverage type"}, "deductible": {"type": "number", "description": "Deductible amount"}, "estimated_damages": {"type": "number", "description": "Total estimated damages"}, "line_items": { "type": "array", "items": {"type": "object", "properties": { "category": {"type": "string"}, "description": {"type": "string"}, "amount": {"type": "number"} }}, "description": "Itemized damages" }, "status": {"type": "string", "description": "Claim status"} } }) with open("claim.pdf", "rb") as f: job = requests.post( f"{BASE}/v1/process", headers=headers, files={"file": f}, data={"schema": schema}, ).json() job_id = job["id"] print(f"Processing: {job_id}") delay = 5 while True: time.sleep(delay) result = requests.get(f"{BASE}/v1/jobs/{job_id}", headers=headers).json() if result["status"] == "completed": fields = result.get("extraction", {}).get("fields", []) print(json.dumps(fields, indent=2)) for f in fields: if f.get("needs_review"): print(f"\n REVIEW: {f['key']} — {f.get('review_reason')}") break elif result["status"] == "failed": print(f"Failed: {result.get('error')}") break delay = min(delay * 1.5, 15) ``` ### cURL ```bash curl -s -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@claim.pdf" \ -F 'schema={"type":"object","properties":{"claim_number":{"type":"string","description":"Claim number"},"policy_number":{"type":"string","description":"Policy number"},"claimant_name":{"type":"string","description":"Claimant name"},"date_of_loss":{"type":"string","description":"Date of loss"},"loss_type":{"type":"string","description":"Type of loss"},"estimated_damages":{"type":"number","description":"Total damages"},"status":{"type":"string","description":"Claim status"}}}' ``` ## Redact Claimant PII Before Sharing Redact claimant personal information before sending to adjusters, contractors, or third-party reviewers: ```python # Step 1: Extract the claim data you need with open("claim.pdf", "rb") as f: extract_job = requests.post( f"{BASE}/v1/process", headers=headers, files={"file": f}, data={"schema": schema}, ).json() # Step 2: Redact PII from the original with open("claim.pdf", "rb") as f: redact_job = requests.post( f"{BASE}/v1/pii/redact", headers=headers, files={"file": f}, ).json() redact_id = redact_job["id"] delay = 5 while True: time.sleep(delay) result = requests.get(f"{BASE}/v1/pii/{redact_id}", headers=headers).json() if result["status"] == "completed": report = result["report"] print(f"Redacted {report['total_redactions']} PII instances") for pii_type, info in report["pii_detected"].items(): print(f" {pii_type}: {info['count']} found") pdf = requests.get(result["redacted_file_url"]).content with open("claim_redacted.pdf", "wb") as f: f.write(pdf) print("Saved: claim_redacted.pdf") break elif result["status"] == "failed": print(f"Failed: {result.get('error')}") break delay = min(delay * 1.5, 15) ``` ## Use Cases - **Claims Processing** — Extract claim numbers, dates, damage descriptions, and amounts from incoming claims - **Policy Document Analysis** — Pull coverage terms, limits, deductibles, and exclusions from policy documents - **EOB Processing** — Extract procedure codes, allowed amounts, patient responsibility from Explanations of Benefits - **Loss Reports** — Parse adjuster field reports for damage categories, estimates, and recommendations - **Subrogation** — Extract third-party liability information and recovery amounts - **Fraud Detection** — Batch-process claims and flag inconsistencies in dates, amounts, or descriptions - **Auto Claims** — Extract vehicle info, driver details, accident descriptions, and repair estimates - **Workers Comp** — Pull injury descriptions, medical provider info, and lost wage calculations ## Tips for Insurance Documents - **Specify claim-specific field names** — Using "Claim number or reference ID" works better than just "number" - **Include status values** — Adding expected statuses (Filed, Under Review, Approved, Denied) in descriptions helps extraction - **Create blueprints for recurring form types** — If you process the same carrier's claim forms repeatedly, train a blueprint at deepread.tech/dashboard/optimizer for 20-30% improvement - **Always redact before external sharing** — Use PII redaction before sending to contractors, adjusters, or legal teams ## BYOK — Zero Processing Costs Connect your own OpenAI, Google, or OpenRouter key via the dashboard. All document processing routes through your provider — zero DeepRead LLM costs, page quota skipped. Set it up: https://www.deepread.tech/dashboard/byok ## Related DeepRead Skills - **deepread-ocr** — General OCR and structured extraction — `clawhub install uday390/deepread-ocr` - **deepread-pii** — Redact PII from any document — `clawhub install uday390/deepread-pii` - **deepread-form-fill** — Fill PDF forms with AI vision — `clawhub install uday390/deepread-form-fill` - **deepread-invoice** — Invoice and receipt processing — `clawhub install uday390/deepread-invoice` - **deepread-medical** — Medical records processing — `clawhub install uday390/deepread-medical` - **deepread-legal** — Legal document processing — `clawhub install uday390/deepread-legal` - **deepread-agent-setup** — OAuth device flow authentication — `clawhub install uday390/deepread-agent-setup` - **deepread-byok** — Bring Your Own Key setup — `clawhub install uday390/deepread-byok` ## Support - **Dashboard**: https://www.deepread.tech/dashboard - **Demo Repo**: https://github.com/deepread-tech/deepread-demo - **n8n Node**: https://www.npmjs.com/package/n8n-nodes-deepread - **Issues**: https://github.com/deepread-tech/deep-read-service/issues - **Email**: support@deepread.tech --- **Get started free:** https://www.deepread.tech/dashboard/?utm_source=clawhub