Claude Code Skills for Insurance Claims Processing

Insurance claims processing involves repetitive document handling, data extraction, validation logic, and compliance checks. These tasks are ideal candidates for automation through Claude Code skills. This guide shows you how to build skills that handle claims intake, document analysis, fraud screening, and settlement calculation. For more domain-specific skill patterns, see the use cases hub.

Understanding Claims Processing Workflows

Claims processing typically follows a predictable sequence: intake, document collection, initial assessment, investigation, approval or denial, and payment. Each stage generates structured and unstructured data that Claude skills can process.

A well-designed claims processing skill handles the document-heavy portions of this workflow. It reads submitted forms, extracts relevant data, validates completeness, checks for fraud indicators, and produces structured output for downstream systems.

Building a Claims Intake Skill

The first skill handles initial claim submission. It accepts uploaded documents, extracts key information, and creates a standardized claim record. The skill .md format guide covers the exact syntax needed to structure these instruction files.

# claims-intake

You process insurance claim submissions. Extract structured data from claim forms and supporting documents.

## Input Handling
Accept PDF claim forms, uploaded photos of damage, and any accompanying notes.
For each submission:
1. Extract policy number, claimant information, incident date, and description
2. Identify the claim type (auto, property, health, liability)
3. Flag any missing required fields

## Data Extraction Rules
- Policy number: 9-12 alphanumeric characters, usually at top of form
- Incident date: Parse from text, convert to YYYY-MM-DD format
- Claim type: Classify from form fields and description text

## Output Format
Return a JSON object with:
{
  "claim_id": "auto-generated",
  "policy_number": "extracted",
  "claimant_name": "extracted",
  "claim_type": "classified",
  "incident_date": "YYYY-MM-DD",
  "description_summary": "50 word max",
  "missing_fields": [],
  "risk_flags": []
}

## Validation
- Verify policy number exists in system
- Check incident date is within policy active period
- Flag claims with no description or no incident date

This skill demonstrates several key patterns: clear input specifications, structured extraction rules, consistent JSON output, and built-in validation. The skill outputs machine-readable data that downstream systems can consume.

Document Validation and Completeness Checking

After intake, claims require validation against policy terms and completeness requirements. Build a skill that automates this verification.

# claims-validator

You validate insurance claims against policy rules and completeness standards.

## Validation Rules
For each claim, check:
1. Required documents present (ID, proof of loss, supporting photos)
2. Policy is active and covers the claim type
3. Deductible met or exceeded
4. Claim filed within policy timeframe
5. No exclusions apply to the incident type

## Document Classification
Classify uploaded documents:
- "identity" - government ID, policyholder verification
- "proof_of_loss" - itemized loss statement, receipts
- "damage_photos" - images showing damage
- "police_report" - incident report if applicable
- "medical_records" - for health claims
- "other" - supporting documentation

## Coverage Logic
Reference policy type:
- Auto: collision, comprehensive, liability coverage check
- Property: dwelling, personal property, liability coverage
- Health: major medical, deductible, co-pay apply

## Output
{
  "validation_status": "complete|incomplete|pending_review",
  "missing_documents": [],
  "coverage_verified": true|false,
  "deductible_met": true|false,
  "exclusions_found": [],
  "next_steps": []
}

The validator skill applies business rules programmatically. It produces deterministic outputs based on clear conditional logic—exactly the pattern Claude Code skills excel at.

Fraud Detection Patterns

Fraud detection combines multiple data points to identify suspicious claims. Build a skill that analyzes claims against known fraud indicators.

# claims-fraud-screener

You screen insurance claims for fraud indicators. Analyze patterns and flag suspicious claims for investigation.

## Fraud Indicators
Flag claims with:
- Late filing after significant delay from incident
- Round-dollar loss amounts
- Inconsistent dates between documents
- Policy recently added or renewed before claim
- Multiple claims on same policy short period
- Unusual claim frequency for policy type

## Pattern Analysis
Cross-reference:
- Claimant history of past claims
- Similar claims in the system
- Geographic patterns (high-fraud areas)
- Adjuster notes for inconsistencies

## Risk Scoring
Assign risk score 0-100 based on:
- 20 points: Single fraud indicator present
- 40 points: Multiple indicators same category
- 60 points: Inconsistencies between documents
- 80 points: History of suspicious claims
- 100 points: Direct fraud match

## Output Format
{
  "risk_score": 0-100,
  "fraud_indicators": ["specific indicators found"],
  "recommendation": "approve|review|escalate",
  "investigation_notes": "specific concerns for adjuster"
}

Fraud screening requires pattern recognition across multiple dimensions. The skill applies scoring logic consistently, ensuring every claim receives objective evaluation.

Claims Adjudication and Settlement

The final processing stage calculates appropriate settlement amounts. Build a skill that computes claim values based on policy coverage and actual loss.

# claims-adjudicator

You calculate insurance claim settlement amounts based on policy coverage and documented losses.

## Settlement Calculation
For approved claims:
1. Start with total documented loss amount
2. Apply policy deductible
3. Apply coverage limits
4. Calculate actual cash value or replacement cost
5. Account for depreciation if applicable

## Coverage Application
- Actual Cash Value: replacement cost minus depreciation
- Replacement Cost: cost to replace with like kind
- Policy Limit: maximum payout per occurrence

## Depreciation Schedules
Apply standard depreciation:
- Vehicles: 20% first year, 15% subsequent years
- Property: 10% per year for contents
- Roof: 10% per year, minimum 50% coverage

## Output
{
  "claim_id": "reference",
  "total_loss": amount,
  "deductible": amount,
  "covered_loss": amount,
  "depreciation": amount,
  "policy_limit": amount,
  "settlement_amount": amount,
  "calculation_breakdown": []
}

Adjudication combines mathematical calculation with policy-specific rules. The skill produces auditable output showing exactly how the settlement was derived.

Integrating Skills into Claims Systems

These individual skills work as components in a larger claims processing pipeline. Connect them through a claims processing skill that orchestrates the workflow:

# claims-processor

You orchestrate the end-to-end insurance claims processing workflow.

## Workflow Sequence
1. Run claims-intake to extract and structure submitted data
2. Run claims-validator to verify completeness and coverage
3. Run claims-fraud-screener to assess risk level
4. Run claims-adjudicator to calculate settlement if approved

## Decision Logic
- If validation fails: return missing items to claimant
- If fraud score exceeds 70: escalate to fraud investigation
- If fraud score 40-70: require adjuster review before proceeding
- If fraud score below 40 and validation passes: calculate settlement
- If claim denied: generate denial letter with explanation

## Output
Produce complete claim file with all processing stage results and final disposition.

This orchestration skill demonstrates how Claude Code skills compose into powerful automation pipelines. Each skill handles a specific domain while the orchestrator manages workflow logic. For patterns on combining multiple skills this way, see how to combine two Claude skills in one workflow.

Best Practices for Claims Skills

When building claims processing skills, follow these guidelines:

Maintain audit trails. Every skill output should include timestamps, processing notes, and clear attribution. Insurance requires documented decision-making.

Validate all inputs. Claims involve legal and financial obligations. Build validation into each skill rather than assuming downstream handling.

Use structured output formats. JSON output enables integration with claims management systems, databases, and reporting tools.

Separate concerns. Distinct skills for intake, validation, fraud, and settlement allow independent testing and modification.

Handle edge cases explicitly. Claims processing involves exceptions. Build explicit handling for appeals, disputes, and special circumstances.

Claude Code skills transform insurance claims processing from manual document handling into automated workflows. Start with the intake skill, validate outputs, then incrementally add validation, fraud detection, and settlement capabilities.

Claude Skill MD Format Complete Specification Guide — write well-structured skill files for each stage of the claims pipeline
How Do I Combine Two Claude Skills in One Workflow — orchestrate intake, validation, fraud, and settlement skills into a single pipeline
Building Production AI Agents with Claude Skills 2026 — deploy claims processing skills into production-grade automated systems
Use Cases Hub — explore other domain-specific automation patterns for regulated industries

Built by theluckystrike — More at zovo.one