Verisk applies Discovery Navigator GenAI to enhances complex claims data into faster, actionable insights for insurers.
Verisk is applying GenAI to one of insurance’s hardest problems, claims data is large, unstructured, and slow to process. Medical demand packages often exceed hundreds of pages. Manual review delays decisions and increases operational costs. Verisk uses GenAI to convert this complexity into structured, searchable insights.
The core challenge lies in unstructured documents. Claims adjusters must extract diagnoses, treatments, and timelines from dense records. This process is repetitive and error-prone. Verisk’s Discovery Navigator uses GenAI to summarize and organize this data. It identifies key medical details and transforms them into structured outputs for faster analysis.
GenAI delivers clear benefits in speed and decision quality. Review time drops by up to 90 percent. Adjusters can query documents instead of reading them line by line. This improves accuracy and consistency across claims. It also enables faster settlements and better negotiation outcomes. The system turns static documents into interactive data assets.
Another key advantage is scalability. GenAI allows insurers to handle growing claim volumes without increasing staff. It also supports continuous updates as new medical procedures emerge. Built on platforms like Amazon Web Services, the solution integrates multiple models to maintain performance. Over time, GenAI becomes embedded into workflows, shifting from efficiency tool to competitive requirement.
Why it matters
GenAI is redefining how enterprises process unstructured data at scale.
• Solves the bottleneck of manual document review in data-heavy industries
• Transforms static records into searchable, decision-ready intelligence
• Signals shift from efficiency gains to revenue and competitive advantage
This case reflects a broader enterprise problem: extracting value from large, unstructured data sources while maintaining accuracy, speed, and compliance.
