AI Agent for Insurance Claims: Intake, Assessment, and Processing Automation

The Insurance Claims Bottleneck

Insurance claims processing is a perfect candidate for AI automation. The typical claim follows a structured workflow — intake, document review, damage assessment, coverage verification, payout calculation — yet most insurers still handle much of this manually. Average processing times of 30 to 45 days frustrate customers and drive up operational costs. An AI agent can compress straightforward claims to minutes while routing complex cases to adjusters with a pre-analyzed package.

Agent Pipeline

1. Claim Intake — structured collection of claim information
2. Document Analysis — extract data from photos, reports, and receipts
3. Coverage Verification — check policy terms and exclusions
4. Payout Calculation — compute the settlement amount

Step 1: Claim Intake Model

Define structured models for claims and supporting documents.

from pydantic import BaseModel
from datetime import date, datetime
from enum import Enum


class ClaimType(str, Enum):
    AUTO = "auto"
    HOME = "home"
    HEALTH = "health"
    LIFE = "life"
    PROPERTY = "property"


class ClaimStatus(str, Enum):
    SUBMITTED = "submitted"
    UNDER_REVIEW = "under_review"
    APPROVED = "approved"
    DENIED = "denied"
    PENDING_INFO = "pending_info"


class ClaimIntake(BaseModel):
    claim_id: str
    policy_number: str
    claimant_name: str
    claim_type: ClaimType
    incident_date: date
    incident_description: str
    estimated_loss: float
    documents: list[str]  # File paths
    submitted_at: datetime


class PolicyDetails(BaseModel):
    policy_number: str
    holder_name: str
    claim_type: ClaimType
    coverage_limit: float
    deductible: float
    exclusions: list[str]
    effective_date: date
    expiry_date: date
    premium_status: str  # "current", "lapsed"

Step 2: Document Analysis

The agent analyzes photos, police reports, medical records, and repair estimates.

from openai import OpenAI

client = OpenAI()


class DocumentAnalysis(BaseModel):
    document_type: str
    key_findings: list[str]
    damage_items: list[dict]  # {"item": str, "estimated_cost": float}
    dates_mentioned: list[str]
    parties_involved: list[str]
    fraud_indicators: list[str]


def analyze_claim_document(
    document_text: str, claim_type: str
) -> DocumentAnalysis:
    """Analyze a claim supporting document."""
    response = client.beta.chat.completions.parse(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": (
                    f"You are an insurance claims analyst specializing "
                    f"in {claim_type} claims. Analyze this document and "
                    f"extract key findings, itemized damage, dates, "
                    f"parties involved, and any potential fraud "
                    f"indicators."
                ),
            },
            {"role": "user", "content": document_text},
        ],
        response_format=DocumentAnalysis,
    )
    return response.choices[0].message.parsed


def analyze_damage_photo(image_path: str, claim_type: str) -> dict:
    """Analyze a damage photo using vision capabilities."""
    import base64

    with open(image_path, "rb") as f:
        image_data = base64.b64encode(f.read()).decode()

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": (
                    "You are an insurance damage assessor. Describe "
                    "the visible damage, estimate severity (minor, "
                    "moderate, severe, total loss), and list specific "
                    "damage items with estimated repair costs."
                ),
            },
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{image_data}"
                        },
                    },
                    {
                        "type": "text",
                        "text": f"Assess damage for {claim_type} claim.",
                    },
                ],
            },
        ],
    )
    return {"assessment": response.choices[0].message.content}

Step 3: Coverage Verification

Check whether the claim is covered under the policy terms.

class CoverageDecision(BaseModel):
    is_covered: bool
    applicable_coverage: str
    coverage_limit: float
    deductible: float
    exclusions_triggered: list[str]
    coverage_notes: str


def verify_coverage(
    claim: ClaimIntake, policy: PolicyDetails
) -> CoverageDecision:
    """Verify claim against policy coverage."""
    exclusions_triggered = []

    # Check policy is active
    if policy.premium_status == "lapsed":
        return CoverageDecision(
            is_covered=False,
            applicable_coverage="None",
            coverage_limit=0,
            deductible=0,
            exclusions_triggered=["Policy lapsed"],
            coverage_notes="Policy premiums not current.",
        )

    # Check incident date is within policy period
    if not (policy.effective_date <= claim.incident_date <= policy.expiry_date):
        return CoverageDecision(
            is_covered=False,
            applicable_coverage="None",
            coverage_limit=0,
            deductible=0,
            exclusions_triggered=["Incident outside policy period"],
            coverage_notes=(
                f"Incident on {claim.incident_date} is outside "
                f"policy period {policy.effective_date} to "
                f"{policy.expiry_date}."
            ),
        )

    # Check exclusions using LLM
    response = client.beta.chat.completions.parse(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": (
                    "Determine if any policy exclusions apply to "
                    "this claim. Be specific about which exclusion "
                    "applies and why."
                ),
            },
            {
                "role": "user",
                "content": (
                    f"Incident: {claim.incident_description}\n\n"
                    f"Policy Exclusions:\n"
                    + "\n".join(
                        f"- {e}" for e in policy.exclusions
                    )
                ),
            },
        ],
        response_format=CoverageDecision,
    )
    decision = response.choices[0].message.parsed
    decision.coverage_limit = policy.coverage_limit
    decision.deductible = policy.deductible
    return decision

Step 4: Payout Calculation

class PayoutCalculation(BaseModel):
    total_claimed: float
    total_approved: float
    deductible_applied: float
    coverage_limit_applied: bool
    net_payout: float
    line_items: list[dict]
    adjustments: list[str]


def calculate_payout(
    analyses: list[DocumentAnalysis],
    coverage: CoverageDecision,
) -> PayoutCalculation:
    """Calculate the claim payout amount."""
    line_items = []
    total_claimed = 0.0

    for analysis in analyses:
        for item in analysis.damage_items:
            cost = item.get("estimated_cost", 0)
            line_items.append({
                "description": item.get("item", "Unknown"),
                "claimed_amount": cost,
                "approved_amount": cost,  # Simplified
            })
            total_claimed += cost

    # Apply deductible
    after_deductible = max(0, total_claimed - coverage.deductible)

    # Apply coverage limit
    limit_applied = after_deductible > coverage.coverage_limit
    net_payout = min(after_deductible, coverage.coverage_limit)

    adjustments = [
        f"Deductible applied: ${coverage.deductible:,.2f}"
    ]
    if limit_applied:
        adjustments.append(
            f"Coverage limit applied: ${coverage.coverage_limit:,.2f}"
        )

    return PayoutCalculation(
        total_claimed=total_claimed,
        total_approved=total_claimed,
        deductible_applied=coverage.deductible,
        coverage_limit_applied=limit_applied,
        net_payout=net_payout,
        line_items=line_items,
        adjustments=adjustments,
    )

Running the Full Pipeline

def process_claim(claim: ClaimIntake, policy: PolicyDetails) -> dict:
    """Process an insurance claim end to end."""
    # Analyze all documents
    analyses = []
    for doc_path in claim.documents:
        text = extract_text(doc_path)  # from earlier tutorial
        analysis = analyze_claim_document(text, claim.claim_type.value)
        analyses.append(analysis)

    # Verify coverage
    coverage = verify_coverage(claim, policy)
    if not coverage.is_covered:
        return {"status": "denied", "reason": coverage.coverage_notes}

    # Calculate payout
    payout = calculate_payout(analyses, coverage)

    return {
        "status": "approved",
        "net_payout": payout.net_payout,
        "line_items": payout.line_items,
        "adjustments": payout.adjustments,
    }

FAQ

How does the agent detect fraudulent claims?

The agent checks for several fraud indicators: inconsistencies between photos and described damage, claims filed shortly after policy inception, duplicate claims across policies, inflated repair estimates compared to market rates, and mismatches between incident descriptions and supporting evidence. High-risk claims are flagged for the Special Investigations Unit.

Can the agent handle complex claims that require expert assessment?

The agent triages claims by complexity. Simple claims (clear documentation, single damage item, within standard parameters) are processed automatically. Complex claims (multiple parties, disputed liability, large losses) are routed to human adjusters with a pre-built analysis package that saves hours of initial investigation.

What about regulatory requirements for claim processing timelines?

Most states require acknowledgment within 15 days and decisions within 30-45 days. The agent tracks these deadlines automatically and escalates claims approaching their statutory deadline. It can also generate the required regulatory correspondence at each stage.

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