Enterprise Insurance Claim AI Platform: An Intelligent Claim Analysis Architecture with Multi-Model Collaboration

This article introduces the Enterprise Claim AI Platform, a production-grade AI reference architecture for the insurance industry. Built on the AWS cloud environment, this architecture integrates RAG retrieval, fraud detection models, and LLM reasoning capabilities through event-driven workflows to achieve automated claim analysis, compliance governance, and system scalability. Its core goal is to address pain points in traditional claim processes such as low efficiency and difficulty in fraud identification, while balancing the needs of automation vs. human supervision and technological innovation vs. regulatory compliance.

Traditional insurance claims rely on manual review, which has problems like low efficiency, strong subjectivity, and difficulty in fraud identification. With the maturity of large models and machine learning technologies, the industry is undergoing intelligent transformation, but AI implementation needs to address challenges such as data security, regulatory compliance, system scalability, decision interpretability, and multi-model collaboration. This platform is a production-grade reference architecture designed to solve these challenges.

The platform adopts an event-driven microservice architecture, with core processes as follows:

1. The claim agent initiates a request via API Gateway; after the Lambda function completes payload validation, it publishes the ClaimAnalysisRequested event to EventBridge;
2. EventBridge routes the event to the Step Functions state machine (the core of process orchestration), which defines a complete analysis process including error handling and retry logic;
3. The AI layer calls services in parallel or serially: OpenSearch retrieves policy terms and historical cases, the SageMaker fraud detection model generates a risk score, and Bedrock's Claude model synthesizes information to generate an analysis report;
4. The evaluation layer checks the compliance of outputs, and decision metadata and audit logs are written to DynamoDB to meet regulatory requirements.

• RAG Retrieval-Augmented Generation: Using OpenSearch vector database and Bedrock knowledge base, convert claim descriptions into vectors, retrieve similar policy terms and historical cases as LLM context to improve output accuracy and reduce hallucinations;
• Fraud Detection Model: The model hosted on SageMaker is trained on historical data, evaluates features such as claim amount deviation and time patterns, and outputs a risk score between 0 and 1;
• LLM Reasoning Engine: The Claude model on Bedrock integrates context and risk scores to generate structured reports (including claim summaries, risk assessments, and action recommendations) to support decision-making and auditing.

Governance and Compliance

• Audit Trail: DynamoDB records the complete decision chain (requests, AI calls, outputs, manual reviews) including metadata such as timestamps and model versions to meet regulatory traceability requirements;
• Human-Machine Collaboration: The evaluation layer sets quality check rules, and abnormal cases are automatically routed to the manual review queue to balance AI efficiency and human judgment.

Application Scenarios

• Automation of standardized initial claim review, significantly reducing processing time;
• Early warning of fraud risks, prioritizing high-risk cases;
• Knowledge base-assisted decision-making, pushing relevant policy provisions and historical precedents to agents.

Future Directions

• Multi-model intelligent routing and fallback: Select Bedrock models based on task complexity, automatically switch when the primary model is unavailable;
• Confidence calibration: The system evaluates its own uncertainty, and low-confidence cases trigger manual intervention;
• Policy reference traceability: LLM outputs explicitly cite policy terms to enhance interpretability;
• Red team evaluation: Regularly test model hallucinations and biases to improve reliability.

Summary

This platform is an example of translating cutting-edge AI technology into an enterprise-level system, balancing automation efficiency with human supervision and innovation speed with compliance requirements. It provides a validated starting point for insurance enterprises' AI transformation, and developers can expand or replace components according to business needs to build intelligent claim systems that suit their own characteristics.