AI Assisted Claims QA

This PoC demonstrates AI-powered quality assurance for insurance claims processing. Users can simulate different claim scenarios and see how AI assists in quality review, anomaly detection, and decision validation to ensure accurate and consistent claims handling.

Target Users

Claims QA Analysts
Claims Supervisors
Claims Managers
Quality Assurance Teams

Business Benefits

Enhanced operational efficiency
Reduced processing time
Improved accuracy and consistency
Better resource allocation

How It Works

Claims quality assurance traditionally requires extensive manual review of claim decisions, documentation, and processes. This PoC showcases how AI can automatically analyze claim scenarios, detect potential issues, and provide quality insights. The system simulates real-world claim scenarios with different loss types and provides AI-driven QA analysis to help ensure consistent, accurate, and compliant claims processing.

In production, when an action on a claim is taken by the adjuster, ECS should trigger an event that sends information to the QA Agent. The QA Agent's response should be sent back to ECS, and the adjuster should be able to see the QA Agent's response in the UI as a notification.

Operational Impact

Automated quality assurance checks
Reduced manual review time
Improved claims accuracy
Enhanced compliance monitoring

Customer Impact

Faster claims resolution
More consistent claim outcomes
Improved customer trust

Technical Highlights

Multi-agent system for claims quality assurance

Event-driven architecture for seamless integration

Real-time notification system for adjuster visibility

Scalable and customizable for different claim types

Under 10s response time from QA Agent to adjuster

Implementation Timeline

Proof Of Concept

2-4 weeks

Pilot Deployment

1-2 months

Full Implementation

3-4 months

Loading claim scenarios...

Business Context

Claims quality assurance is critical for maintaining accuracy, compliance, and customer satisfaction. Traditional QA processes are manual, time-consuming, and prone to inconsistency. AI-assisted QA can automatically analyze claim patterns, identify potential issues, and ensure consistent review standards.

Problem Statement

Claims teams need an automated QA system that can analyze claim scenarios in real-time, detect potential fraud indicators, ensure compliance with company policies, and maintain consistent quality standards across all claim types and adjusters.

Impact and Importance

Automated QA reduces the risk of errors, ensures regulatory compliance, improves claim consistency, and frees up QA analysts to focus on complex cases requiring human judgment. This leads to better customer outcomes and reduced operational risk.

Developer Setup

To set up and run this PoC locally, follow these steps:

Ensure you have Python 3.11.11 and Quart installed on your system.
Clone the Claims QA repository and install dependencies:
```
pip install -r src/requirements.txt
```
Start the Flask service with the scenarios API:
```
python src/app.py
```
The API endpoint /api/claims/scenarios will provide claim scenarios data.

How to Use This PoC

Follow these steps to explore the Claims QA functionality:

Browse the available claim scenarios (simulated data) in the main interface.
Click on a scenario card to view detailed claim information and history.
Use checkboxes to include/exclude specific historical actions from the analysis.
Optionally modify the current action (type, description, timestamp) for simulation testing.
Use the "Audit Action" button to trigger AI-powered quality assessment.
Review the QA results including compliance status, risk assessment, and recommendations.