AI Agent Workflows for Insurance Claims Processing

Solution Overview

Agentic AI is transforming the insurance industry, enabling autonomous systems to perceive, reason, and act independently. AI agents are autonomous, allowing them to be goal-driven and operate without precise instructions. Insurers are heavily investing in these technologies to overcome legacy system limitations, deliver personalized customer experiences, and to capitalize on the $80 billion AI insurance market projected by 2032.

Efficient claims processing is important when modernizing the insurance industry. AI tools like NLP, image classification, and vector embedding help insurers perform the following tasks:

• Generate precise catastrophe impact assessments.

• Expedite claims routing with richer metadata.

• Prevent litigation with better analysis.

• Minimize financial losses using more accurate risk evaluations.

However, applying AI to production scenarios can present challenges when balancing reliability and flexibility. Too much autonomy can lead to unpredictable outcomes, while overly restrictive constraints can reduce the autonomy of agents.

To overcome these challenges, this solution shows how an AI agent can help you streamline claims operations and enhance customer satisfaction with a multi-step claims processing workflow. In this workflow, the agent handles accident photos, evaluates damage, and verifies insurance coverage. LLMs analyze policy information and related documents retrieved from MongoDB Atlas Vector Search. The outcomes are then stored in a MongoDB Atlas database.

Reference Architecture

To help agents understand their context, craft a prompt that describes their scope and objectives when you define your agent instance. This solution uses the following prompt:

"You are a claims handler assistant for an insurance company. Your goal is to help claim handlers understand the scope of the current claim and provide relevant information to help them make an informed decision. In particular, based on the description of the accident, you need to fetch and summarize relevant insurance guidelines so that the handler can determine the coverage and process the claim accordingly. Present your findings in a clear and extremely concise manner.”

In addition to defining tasks, you need to specify which tools the agent can use and how to use them. This system uses Vector Search and writes to the database, as represented in the image below.

Figure 1. Steps of the agentic workflow

Vector Search maps the vectorized image description to the related vectorized policy, which includes the coverage description for that class of accident. The agent uses the policy and the related coverages to recommend next actions and assign a work order to a claims handler. Then, it writes to the database to store this information.

Additionally, the solution uses the following technologies:

• Cohere’s embedding model.

• Anthropic’s LLM as the agent’s brain.

• LangGraph as the agentic framework.

Build the Solution

To replicate this solution, follow the instructions in the README of this solution's GitHub repository. This process includes the following steps:

1. Log in to MongoDB Atlas and create your database.

2. Create the specified collections.

3. Set up your Vector Search index.

4. Create an AWS account and set up your Bedrock models.

5. Set up and run the backend.

6. Set up and run the frontend.

7. Configure Docker containers.

After following these steps, you can run the app.

Key Learnings

• AI agents simplify claims processing: AI agents automate the process of finding policies and coverages, eliminating the need to navigate multiple systems, read lengthy PDFs, and summarize information.

• Agentic AI can drive transformative change: AI agents have an unprecedented level of autonomy with their capabilities to reason, perceive, and act. Insurers must embrace experimentation and integrate these technologies into their systems and processes to remain competitive.

• MongoDB's flexible document model enables easy data access: Agents can easily access structured and unstructured data stored in MongoDB by using APIs or the MongoDB MCP server. This allows agents to handle complex and contextual interactions.

Authors

• Luca Napoli, MongoDB

Learn More

• AI-Enhanced Claim Adjustment for Auto Insurance

• AI-Powered Call Center Intelligence with MongoDB

• Claims Management Using LLMs and Vector Search for RAG