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Generative AI > Google Gemini API > Insurance Claims Processing Agent

Insurance Claims Processing Agent

Author: Venkata Sudhakar

Insurance claims processing involves verifying policy details, assessing damage, checking coverage limits, and deciding on settlements - a multi-step workflow well suited to an AI agent. A Gemini-powered agent can handle first notice of loss intake, validate policy terms, flag suspicious patterns, and generate settlement recommendations automatically.

ShopMax India's device protection insurance division receives hundreds of claims daily for damaged or stolen electronics. Manual processing takes 3-5 days. An automated agent reduces this to minutes for straightforward claims while routing complex cases to human adjusters.

The below example shows an insurance claims processing agent that validates a claim, checks policy coverage, and produces a settlement recommendation.

import google.generativeai as genai
import json

genai.configure(api_key="your-api-key")
model = genai.GenerativeModel("gemini-2.0-flash")

# Policy database
POLICIES = {
    "POL-2024-001": {
        "customer": "Rajesh Kumar",
        "city": "Mumbai",
        "device": "Laptop",
        "coverage": "accidental_damage",
        "max_claim": 45000,
        "deductible": 2000,
        "active": True
    },
    "POL-2024-002": {
        "customer": "Priya Sharma",
        "city": "Bangalore",
        "device": "Smartphone",
        "coverage": "theft",
        "max_claim": 30000,
        "deductible": 1500,
        "active": True
    }
}

def process_claim(policy_id, claim_type, damage_description, estimated_repair_cost):
    # Validate policy
    if policy_id not in POLICIES:
        return {"status": "rejected", "reason": "Policy not found"}
    policy = POLICIES[policy_id]
    if not policy["active"]:
        return {"status": "rejected", "reason": "Policy inactive"}

# Use Gemini to assess the claim
    prompt = f"""You are an insurance claims assessor for ShopMax India device protection.
Policy: {json.dumps(policy, indent=2)}
Claim type: {claim_type}
Damage description: {damage_description}
Estimated repair cost: Rs {estimated_repair_cost}

Assess this claim:
1. Is the claim type covered under this policy?
2. Are there any red flags suggesting fraud?
3. Calculate the settlement amount (max_claim - deductible, capped at estimated_repair_cost)
4. Recommend: approve, reject, or escalate to human adjuster
Respond in JSON with keys: covered, red_flags, settlement_amount, recommendation, reason"""

response = model.generate_content(prompt)
    # Extract JSON from response
    text = response.text.strip()
    start = text.find("{")
    end = text.rfind("}") + 1
    assessment = json.loads(text[start:end])
    assessment["policy_id"] = policy_id
    assessment["customer"] = policy["customer"]
    return assessment

# Test the agent
result = process_claim(
    policy_id="POL-2024-001",
    claim_type="accidental_damage",
    damage_description="Laptop dropped from desk, screen cracked, keyboard not working",
    estimated_repair_cost=18000
)
print(json.dumps(result, indent=2))

It gives the following output,

{
  "covered": true,
  "red_flags": "None identified - damage description consistent with accidental drop",
  "settlement_amount": 16000,
  "recommendation": "approve",
  "reason": "Accidental damage covered. Settlement = Rs 18000 (repair) - Rs 2000 (deductible) = Rs 16000",
  "policy_id": "POL-2024-001",
  "customer": "Rajesh Kumar"
}

The agent validates policy coverage, applies deductibles, and flags unusual patterns before recommending a settlement. Extend it with document upload for repair invoices, integration with a claims management system, and a batch processing loop to handle the full daily claims queue automatically.

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