I Built an Autonomous Insurance Claims Agent (Because I Hate Paperwork)

I Built an Autonomous Insurance Claims Agent (Because I Hate Paperwork)

Simulating a 5,000-claim workload with a Python Agent Swarm to automate the boring stuff.

TL;DR

I simulated an entire insurance claims department on my laptop. Using Python and a multi-agent swarm architecture, I processed thousands of mock claims, detecting fraud and verifying policy limits automatically. The system uses a "Swarm" of specialized agents (Intake, Fraud, Policy, Decision) to handle a long-running batch process that would take humans weeks to complete manually. No humans were bored in the making of this project.

Introduction

I have observed that one of the most persistent bottlenecks in modern business isn't "strategy" or "innovation"—it's the sheer volume of mundane decision-making. In my opinion, industries like insurance, logistics, and finance are drowning in what I call "Type 1 Decisions": decisions that follow a strict rule set but are trapped in unstructured data formats like PDF claims or email text.

I’ve always been fascinated by these "boring" back-office problems. You know, the kind where someone manually reviews document after document, looking for a date mismatch or a missing signature. It feels like the perfect job for an AI. But not just a chatbot. A chatbot can answer "What is the policy limit?", but it can't sit there for 8 hours and process 5,000 files.

So, I asked myself: Could I build a swarm of AI agents to handle this strictly as an autonomous batch process?

In my opinion, the best way to learn is to build. So I decided to create ClaimsIntelAgent, a Python-based system where specialized agents pass claims around like a hot potato until they are approved, rejected, or flagged for fraud.

What's This Article About?

This isn't just a "Hello World" for AI agents. This is a deep dive into building a functional, logic-driven processing engine.

• The Problem: High-volume, rule-based text processing.
• The Solution: A multi-agent system where roles are clearly defined.
• The Outcome: A system that processes thousands of transactions in minutes, visualizing the "Risk" vs. "Value" of every claim.

I wrote this because I wanted to move beyond the hype of "GenAI will do everything" and show what "Agentic AI" looks like when applied to a rigid business process.

Tech Stack

From my experience, you don't need a complex cloud architecture for a Proof of Concept. I chose a stack that emphasizes readability and visualization.

• Python 3.12: The engine. Robust, typed (mostly), and perfect for this logic.
• Rich: For that beautiful terminal UI you see in the GIF. If you can't see the progress, you can't trust the agent.
• Faker: To generate realistic (but fake) claim data. We need messy data to test the agents.
• Matplotlib/Seaborn: For generating the end-of-run reports. Data is useless without visualization.

Why Read It?

If you’re interested in:

1. Agentic Workflows: Moving beyond simple "chatbots" to task-oriented agents that perform specific jobs.
2. Logic-Based AI: Combining simple rules (if value > X) with swarm patterns to create complex systemic behavior.
3. Python Engineering: Structuring a project that looks and feels professional, with logging, error handling, and visual feedback.

Let's Design

I didn't want a monolith. A single process_claim() function would be unmaintainable. In my opinion, a "Swarm" approach is better because it allows us to upgrade individual "Agents" without breaking the whole chain.

The flow I designed is simple but robust:

1. Intake Agent: The gatekeeper. It checks if the claim is valid JSON, has the required IDs, and isn't malformed.
2. Fraud Agent: The detective. It looks for red flags (e.g., claim amount > policy limit, frequent filer, location mismatches). It assigns a "Risk Score" (0-100).
3. Policy Agent: The lawyer. It checks the fine print. Does "Auto Basic" cover a $50,000 accident? (Spoiler: No).
4. Decision Agent: The judge. It takes inputs from everyone else (Validity, Risk Score, Coverage) and stamps the final verdict.

I thought about using an LLM for every step, but I realized that for things like "Check if Amount > Limit", code is faster and more accurate than an LLM. So this hybrid approach validates the idea that Code > LLM for arithmetic and boolean logic.

Let's Get Cooking

Here is how I structured the "Brain" of the operation. This isn't just pseudo-code; this is the actual structure I ran.

The Mock Data Generator

First, we need data. I used Faker to create a mess of 5,000 claims. Some needed to be valid, some fraudulent, some just broken.

def generate_mock_claims(count=5000):
    """Generates a large batch of mock insurance claims."""
    claims = []
    for i in range(count):
        claim = {
            "claim_id": f"CLM-{fake.uuid4()[:8].upper()}",
            "policy_type": random.choice(POLICY_TYPES),
            "claim_amount": round(random.uniform(50, 50000), 2),
            "prior_claims": random.randint(0, 5)
            # ... and more fields
        }
        claims.append(claim)
    return claims

The Agents

I created a base class and then specialized it. The FraudDetectionAgent is my favorite. In my opinion, fraud detection is rarely a single "Aha!" moment; it's an accumulation of small details.

class FraudDetectionAgent(BaseAgent):
    def analyze(self, claim):
        risk_score = 0
        notes = []

        # Rule 1: High Amount
        # If a claim is huge, it deserves a closer look.
        if claim['claim_amount'] > 40000:
            risk_score += 40
            notes.append("High Value Claim")

        # Rule 2: Rapid Claims
        # Someone claiming 5 times in a year is suspicious.
        if claim['prior_claims'] > 3:
            risk_score += 30
            notes.append("Frequent Claimant")

        # Rule 3: Missing Evidence
        if not claim['evidence_attached']:
            risk_score += 20
            notes.append("No Evidence Attached")

        return risk_score, notes

It’s simple logic, but when you scale it to 5,000 claims, it becomes a powerful filter. A human might miss that "Prior Claims: 4" line on page 3 of a PDF. The agent never misses it.

The Orchestrator

To make it feel like a real long-running process, I built a main loop using rich. This is where the magic happens. The orchestrator doesn't know how to process a claim; it just knows who can process it.

# The Execution Loop
    with Live(table, refresh_per_second=10) as live:
        for idx, claim in enumerate(claims_batch):
            # Step 1: Intake
            is_valid, msg = intake_agent.process(claim)

            # Step 2: Fraud Check
            # The fraud agent returns a score, not a decision.
            risk_score, fraud_notes = fraud_agent.analyze(claim)

            # Step 3: Policy Check
            # The policy manager strictly checks the contract.
            is_covered, policy_note = policy_agent.verify(claim)

            # Step 4: Decision
            # The Decision Agent aggregates all previous signals.
            result = decision_agent.decide(claim, risk_score, is_covered, policy_note)

            # Update Dashboard
            table.add_row(
                result.claim_id,
                f"${claim['claim_amount']:,.2f}",
                f"Risk: {risk_score}",
                result.status
            )

I implemented a DecisionAgent that aggregates the logic. This is critical. You don't want the Fraud Agent rejecting claims; you want it flagging them. The Decision Agent makes the final call.

class DecisionAgent(BaseAgent):
    def decide(self, claim, risk_score, policy_valid, policy_note):
        # 1. Policy Violation is an instant reject
        if not policy_valid:
            status = "REJECTED"
        # 2. High Risk is a fraud flag
        elif risk_score > 70:
            status = "FLAGGED_FRAUD"
        # 3. Moderate risk goes to human review
        elif risk_score > 30:
            status = "MANUAL_REVIEW"
        # 4. Clean claims are auto-approved
        else:
            status = "APPROVED"

        return ClaimResult(status=status, ...)

Let's Setup

If you want to run this yourself, I made it extremely easy.

1. Clone the Repo: git clone https://github.com/aniket-work/claims-intel-agent
2. Environment: Create a venv (always use virtual environments!).
3. Install: pip install -r requirements.txt.
4. Run: python main.py.

Step by step details can be found at: GitHub Repository README

Let's Run

When I run this, it feels like I'm sitting in a mission control center. The terminal comes alive.

The logs scroll past:
[Info] Processing CLM-A1B2... Risk Score: 0... APPROVED
[Info] Processing CLM-C3D4... Risk Score: 85... FLAGGED_FRAUD

After processing 125 sample claims (for the detailed verified run), the system generated this distribution:

As you can see, the majority of claims (Green) are approved. This is the "Happy Path". But look at the sheer number of "Manual Review" (Yellow) and "Rejected" (Red) items. These are the ones where the agent saved the company money.

I also generated a Risk Analysis scatter plot to verify my logic.

In my opinion, this chart is the most critical part of the project.

• The X-axis is the Claim Amount.
• The Y-axis is the Risk Score.
• The Colors are the Status.

Notice how the purple dots ("FLAGGED_FRAUD") cluster at the top? That's the agent working. It's identifying that claims with specific characteristics (high amount, frequent history) map effectively to the "Fraud" zone. You can also see "Manual Review" (the orange-ish dots) in the middle ground. This confirms that my threshold logic (risk > 30 and risk > 70) is correctly segmenting the population.

Closing Thoughts

This experiment reinforced my belief that "Agentic AI" isn't just about LLMs writing poetry or code. It's about structuring robust, logical systems that can handle the sheer scale of modern data.

I wrote this, and then I thought: What if we connected this to a vision model that actually reads the evidence images? That's a project for another day. But for now, ClaimsIntelAgent proves that with just a few hundred lines of Python, you can build a system that simulates the work of an entire department.

The full code is open source. Go clone it, break it, and build something cool.

aniket-work / claims-intel-agent

Autonomous Agent Swarm for batch processing insurance claims. An experimental PoC.

Autonomous Insurance Claims Processing Agent 🕵️‍♂️💼

📌 Overview

This project is an experimental AI Swarm System designed to handle high-volume insurance claims processing. It simulates a long-running batch workload where multiple specialized agents (Intake, Fraud Detection, Policy Coverage, Decision) collaborate to analyze claims data and make compliance decisions.

Disclaimer: This is a Proof of Concept (PoC) for educational purposes. It demonstrates agentic workflows and is not a production-ready insurance system.

🚀 Features

• Multi-Agent Architecture: Specialized roles for distinct processing steps.
• Batch Processing Simulation: Handles thousands of claims in a continuous loop.
• Fraud Detection Engine: Logic-based rules to flag suspicious patterns.
• Rich Terminal UI: Real-time progress tracking with rich library.
• Statistics & Reporting: Generates detailed JSON reports and visual charts.

🛠️ Architecture

📥 Installation

git clone https://github.com/aniket-work/claims-intel-agent.git
cd claims-intel-agent
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

⚡ Usage

Run the main orchestrator:

…

View on GitHub

Disclaimer

The views and opinions expressed here are solely my own and do not represent the views, positions, or opinions of my employer or any organization I am affiliated with. The content is based on my personal experience and experimentation and may be incomplete or incorrect. Any errors or misinterpretations are unintentional, and I apologize in advance if any statements are misunderstood or misrepresented.