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

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

How I used Python, AI Agents, and a little curiosity to process 5,000 mock claims in minutes.

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. No humans were bored in the making of this project.

Introduction

I’ve always been fascinated by "boring" back-office problems. You know, the kind where someone manually reviews PDF after PDF, looking for a date mismatch or a missing signature. It feels like the perfect job for an AI.

So, I asked myself: Could I build a swarm of AI agents to handle this?

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.

Why Read It?

If you’re interested in:

1. Agentic Workflows: Moving beyond simple "chatbots" to task-oriented agents.
2. Logic-Based AI: Combining simple rules with swarm patterns.
3. Python Engineering: Structuring a project that looks and feels professional.

Tech Stack

• Python 3.12: The engine.
• Rich: For that beautiful terminal UI you see in the GIF.
• Faker: To generate realistic (but fake) claim data.
• Matplotlib/Seaborn: For generating the end-of-run reports.

Let's Design

I didn't want a monolith. I wanted a "swarm" where each agent had one job.

The flow is simple but effective:

1. Intake Agent: Checks if the claim has the basic necessary fields.
2. Fraud Agent: Looks for red flags (e.g., claim amount > policy limit, frequent filer).
3. Policy Agent: Verifies if the policy actually covers the incident.
4. Decision Agent: The judge. It takes inputs from everyone else and stamps the final verdict.

Let's Get Cooking

Here is how I structured the "Brain" of the operation.

The Agents

I created a base class and then specialized it. Here is the FraudDetectionAgent in action:

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

        # Rule 1: High Amount
        if claim['claim_amount'] > 40000:
            risk_score += 40
            notes.append("High Value Claim")

        # Rule 2: Rapid Claims
        if claim['prior_claims'] > 3:
            risk_score += 30
            notes.append("Frequent Claimant")

        return risk_score, notes

It’s simple logic, but when you scale it to 5,000 claims, it becomes a powerful filter.

The Orchestrator

To make it feel like a real long-running process, I built a main loop using rich to visualize the progress.

# The Loop
    with Live(table, refresh_per_second=10) as live:
        for idx, claim in enumerate(claims_batch):
            # Agent Pipeline
            is_valid, msg = intake_agent.process(claim)
            risk_score, fraud_notes = fraud_agent.analyze(claim)
            is_covered, policy_note = policy_agent.verify(claim)

            # Final Decision
            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
            )

Let's Run

When I run this, it feels like I'm sitting in a command center.

The system churns through the backlog, and at the end, I get a clear report. In this run, we auto-approved about 60% of claims, which would save a human team hundreds of hours.

Closing Thoughts

This experiment reinforced my belief that "Agentic AI" isn't just about LLMs writing poetry. It's about structuring code to make autonomous decisions based on data.

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.