Automated Insurance claims processing using Amazon Bedrock, Knowledge Base, and Agents

In the dynamic insurance industry, companies are constantly seeking ways to streamline their operations and deliver exceptional customer experiences. One such approach is the automation of the insurance claims processing lifecycle using various AWS services. In this blog, we’ll explore how to leverage the capabilities of Amazon Bedrock with Generative AI to operate Large Language Models (LLMs), ensuring data security and privacy. By integrating Bedrock’s robust AI features, insurance companies can streamline processes and enhance customer experiences. This technical article will explore the AWS architecture and the key services that enable this powerful automation. There is an associated demo and deployment solution in this Github repository.

Understanding Amazon Bedrock

Amazon Bedrock is a fully managed service that provides access to a variety of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, and Mistral AI. This allows customers to experiment easily with and evaluate different models for their use cases. it provides a scalable platform for building Generative AI-powered applications securely. Amazon Bedrock keeps customer data private and doesn’t use it to train or fine-tune the base models, protecting the confidentiality and integrity of sensitive information. It’s designed to facilitate dynamic, context-aware responses to customer inquiries, simulating human-like interactions that offer real-time solutions and advice. By analyzing large volumes of data, the Large Language Model (LLM) can provide personalized service options based on individual customer profiles and historical data. Amazon Bedrock keeps your data under your control. When you tune a foundation model, we base it on a private copy of that model. This means your data is not shared with model providers. Finally, Bedrock is in scope for common compliance standards, including ISO, SOC, CSA STAR Level 2, is HIPAA-eligible, and customers can use Bedrock in compliance with the GDPR.

Knowledge Bases in Action

A key feature of Amazon Bedrock is its ability to create and manage extensive Knowledge Bases. Knowledge Bases for Amazon Bedrock provide fully managed Retrieval Augmented Generation(RAG) to supply the agent with access to your data. These Knowledge Bases are crucial for providing context and increasing the accuracy of responses related to complex insurance policies and regulations. They allow generative AI systems to access a vast array of structured information, enabling them to provide informed responses which helps subject matter expert to make intelligent decisions during the claims process.

Architecture Overview

The reference architecture provided showcases a comprehensive solution that leverages the capabilities of multiple AWS services to handle the end-to-end claims processing workflow.

Front-end Integration:

The process begins with the mobile client, where customers can start insurance claims. AWS Amplify is a service to develop and deploy cloud-powered mobile and web apps, which enables seamless integration between the client and the AWS services, providing secure and efficient data exchange. A typical claim will contain a filled form and, optionally, images to accompany the claim.

Authentication and Authorization:

Amazon Cognito plays a crucial role in managing user authentication and authorization, ensuring that only authorized individuals can access and interact with the claims processing system. Amazon Cognito can be federated to an existing authentication system or securely embedded in an existing application, providing flexibility and enhancing security integration.

Secure Document Storage with Amazon S3:

During the claims process, various types of documents need to be ingested and stored securely. Amazon Simple Storage Service (Amazon S3) can handle this by storing documents such as claim forms, photographs of damage, invoices, and any other relevant files. It securely uploads these documents into encrypted S3 buckets, ensuring they are accessible for processing while maintaining data privacy and integrity. Point the Knowledge Base to your Amazon S3 data source.

Start claim processing:

Amazon API Gateway acts as the entry point for API requests from the mobile application to the backend. It handles the tasks involved in accepting and processing concurrent API calls. In insurance claims processing, the API Gateway provides secure and efficient communication between the mobile client and the backend services, triggering the functions to process the claims such as generate new claim, upload documents, submit the claim, etc.

Step 1. Claim processing job

In the automobile insurance industry, the claim processing workflow starts with the insured reporting the incident and submitting necessary documents. The insurer verifies these documents and conducts an initial assessment. It then assigns a claims adjuster to inspect the damage. AI tools help detect fraud and validate the claim. A detailed repair estimate is prepared and reviewed. If the claim is approved, repairs are authorized and payments are processed. The policyholder is kept informed throughout the process. After a final review, the claim is closed, and feedback is requested to improve future processes.

As claim processing job requires several steps performed by multiple backend components, it requires a job orchestrator. The AWS Step Functions service coordinates parallel processes, exception handling, retries, and timeouts based on the specified business logic, eliminating the need to orchestrate manually application components. It also automatically handles errors and restarts to ensure that application tasks are executed as expected, reducing the number of failed user requests. With the native integration between Step Functions and Bedrock, Step Functions provide a streamlined, reliable, and efficient claims process by managing the claim processing workflow.

Storing the claim

Submitted form and links to the images are stored in the data warehouse or operational claims database.

Step 2. Intelligent Claims Processing using Amazon Bedrock

At the heart of this automated insurance claims processing solution lies the integration of Amazon Bedrock, a powerful platform that enables the deployment of large language models and generative AI capabilities. Multi-modal foundational models such as Anthropic Claude 3 Sonnet are especially beneficial for claims processing as they can accept both text and images data as an input and handle image classification and understanding tasks. Amazon Bedrock can identify the type of claim and cause of loss, compare it against the existing coverage, and support document classification for precise file naming and storage in the document management system. It can pinpoint areas of damage to assist the claim handler in making informed decisions.

Bedrock offers a Retrieval Augmented Generation (RAG) feature, which integrates the foundation models with internal data sources through Amazon Bedrock Knowledge Bases, making the responses more contextual and accurate.

For example, using Amazon Bedrock Knowledge Base to store insurance policy documents allows for automated checks of the claim against the policy, including specific policy excerpts to explain the decision. In an auto claim scenario, the system can automatically identify policy exclusions, analyze the customer and police reports to determine fault, review images to assess damages, and perform fraud checks on the vehicle. This automation ensures thorough claim evaluations.

Amazon Bedrock also includes Agents, which are generative AI programs that can automate multi-step tasks by orchestrating actions, using Knowledge Bases, and generating responses based on user queries. For claims processing, besides policy validation, more information about the customer is required to assess the claim, such as the history of previous claims or customer details.

To increase responsible AI development, Amazon Bedrock provides Guardrails, which allow configuring rules to control denied topics, content filtering, and privacy protection, aligning the AI applications with organizational policies and ethical standards. In an auto insurance claim, Amazon Bedrock’s guardrails ensure AI processes claims fairly by preventing bias, protecting sensitive data, and complying with regulations. This ensures that decisions are ethical, data is secure, and the claimant’s rights are upheld, building trust in the automated claims process.

Let’s take an auto insurance claim as an example and see how the processing pipeline can be streamlined with the help of Generative AI technology.

Step 3. Human in the Loop

AI models, while highly capable, are still evolving to match human accuracy and can still produce inaccurate, biased, or unreliable content. Techniques like Retrieval-Augmented Generation (RAG) and prompt engineering can help to mitigate these issues and minimize the likelihood of hallucinations. RAG integrates large language models with structured data sources for accurate responses. In insurance claims processing, using RAG with Amazon Bedrock’s Knowledge Bases allows AI to access specific policy information, reducing errors. Prompt engineering improves accuracy by designing prompts with simple instructions, guiding the model to generate precise outputs. Together, these methods enhance AI reliability, increasing higher accuracy and consistency in automated workflows.

Subject matter experts can review the generated claim assessment report to ensure it aligns with facts, company policies, and ethical guidelines and make necessary adjustments if required.

Benefits of Automated Insurance Claims Processing with AWS

Integrating AWS services, including Amazon Bedrock, Knowledge Bases, and Agents for Amazon Bedrock, unlocks many benefits for insurance companies:

1. Increased Efficiency: Automation reduces the time and effort required to process claims, improving accuracy and reducing error rates, and reducing the financial effects of open reserves.

2. Enhanced Customer Experience: Faster claims processing and personalized responses lead to higher customer satisfaction and loyalty.

3. Operational efficiency: Automation decreases the manual effort involved in claims handling, increasing speed to value for less experienced claims handlers, and allowing more tenured resources to focus on more complex claims. In addition, AWS serverless technologies enable automatic scaling to adjust resource use dynamically. This optimizes expenses and enhances developer productivity by allowing them to focus on adding business value rather than infrastructure management.

4. Proactive Regulatory Compliance: The integrated system, comprising Amazon Bedrock, Knowledge Bases, and Intelligent Agents, can automatically detect and suggest changes based on the latest legislative updates. For instance, when new legislation is passed that affects insurance operations, Amazon Bedrock, using its automated scanning capabilities of regulatory websites or data sources, can identify these changes. Once a new regulation is detected, it can notify human reviewers, who can then assess and approve the adjustments. The system can then propose modifications to the operational framework to ensure compliance. This proactive approach ensures that the company stays current with regulatory changes, protecting against potential legal challenges and fines, and maintaining trust and reliability with customers and regulatory bodies.

Conclusion

In this post, we explored a solution to automate the insurance claims processing lifecycle and how Amazon Bedrock could help insurance companies through a strategic shift towards advanced automation. Amazon Bedrock, alongside Knowledge Bases and Intelligent Agents, seamlessly manages the claims lifecycle from initiation to fulfillment. Knowledge Bases store up-to-date policies, enabling Agents to extract data, make informed decisions, and deliver accurate, personalized responses to customers, enhancing efficiency and satisfaction. The solution and building blocks can be found and deployed as part of our GitHub repository, or adjusted for your specific use-case needs and model choice.

For businesses, this translates into reduced operational costs, optimized resource allocation, and an elevated customer experience—key drivers for competitive advantage in today’s evolving insurance market.