Case Study: GenAI Claims Processing

Insurance

GenAI Claims Processing

The Challenge: Operational Inefficiencies

A leading Property & Casualty insurer was experiencing severe operational challenges in their claims processing operations. Basic coverage claims were taking over 15 days to resolve, while processing costs had surged by 65% over three years. Customer satisfaction had plummeted with a 22-point decline, and the company was struggling with a 35% annual adjuster turnover rate. Quality issues were rampant, with 40% of claims requiring rework due to errors. These problems were particularly acute during catastrophe events, when massive backlogs would develop, further straining resources and damaging customer relationships. The combination of high costs, poor service, and employee burnout was threatening the insurer's market position and profitability.

Our Solution: AI-Powered Claims Automation

Lydatum designed and deployed a comprehensive GenAI-powered claims automation solution that transformed the entire claims journey. The solution consisted of three main components:

• Intelligent Claims Processing Engine: We integrated Anthropic Claude 3.5's LLM capabilities with Google Document AI to create a powerful claims analysis system. The engine could automatically extract and validate information from claims documents, while advanced TensorFlow computer vision models assessed damage from photos and videos. This was all orchestrated through a modern data architecture combining Google BigQuery, PostgreSQL, and MongoDB for optimal performance.
• Legacy System Integration: Using UiPath, we created seamless integrations with existing systems, ensuring the new AI capabilities could work alongside established processes. Datadog provided comprehensive monitoring of the entire system, while MLflow managed model versioning and deployment.
• Stakeholder-Centric Implementation: The solution was developed through extensive collaboration with all stakeholders. This included comprehensive claims journey mapping, strategic planning sessions with C-suite executives, and voice-of-customer interviews with over 150 claimants. A 15-member adjuster advisory board provided crucial insights for the design, while a phased implementation approach starting with traffic collisions allowed for careful refinement of the system.

Throughout the implementation, we maintained a strong focus on the human element, developing a comprehensive training and career transition program that helped adjusters evolve into higher-value roles focused on complex claims and customer relationships.

The Impact: Transformative Efficiency and Service Improvements

The implementation of the GenAI claims processing solution delivered exceptional results across all key metrics:

The benefits extended well beyond pure efficiency gains. Damage estimate accuracy improved by 40%, while claims leakage was reduced by 25%, directly impacting the bottom line. Customer satisfaction saw a remarkable 25-point increase as claimants experienced faster, more accurate service. Perhaps most notably, adjuster turnover plummeted from 35% to 12% as their roles evolved to focus on more engaging, complex work. The new system also proved invaluable during catastrophe events, maintaining high service levels even under extreme conditions.

Technologies Used: Google BigQuery, Anthropic Claude 3.5, Google Document AI, TensorFlow, PostgreSQL, MongoDB, UiPath, Datadog, MLflow