20 Years of Insurance Data, Digitized with AI

Business Requirement

An insurance company with over 20 years of documentation—spanning policy agreements, claims data, and regulatory filings—needed a solution to organize, extract insights, and reveal hidden patterns within their vast knowledgebase. The aim was to make correlations between conditions, regions, and policies more accessible, enabling better decision-making and operational efficiency.

Solution

Our AI-driven solution addressed these challenges by digitizing and tagging a wide range of insurance documents from various sources, including agencies, carriers, and brokers:

1. Document Classification: We categorized millions of documents such as auto insurance claims, health policy agreements, and regulatory filings. This enabled seamless navigation across historical records.
2. Concept Tagging: The AI model automatically tagged essential concepts like claim conditions (e.g., fire damage, medical conditions), policy types (e.g., auto, health, life), and geographic regions (e.g., state, city, zip codes). These tags allowed for quick filtering of relevant data.
3. Entity Recognition: We identified key entities such as policyholders, insurers, agents, and service providers. For example, a policyholder in Miami who filed multiple claims under different policies would be linked, providing a comprehensive view of their relationship with the insurer.
4. Relationship Mapping and Insights: A key strength of the system was its ability to map relationships across entities and tags, revealing patterns and correlations. For instance, the AI identified a higher frequency of auto insurance claims due to hurricane damage in specific regions like Florida. This was made possible through the relationship graph, which connected the condition “hurricane damage” with the region “Florida” and cross-referenced it with various policies, insurers, and policyholders.
   Another example was identifying the correlation between fraudulent claims and specific agents in particular regions. By tagging claims as “fraudulent” and mapping relationships to agents and locations, the company could target investigation efforts in high-risk areas.

Impact

The company could now effortlessly correlate claim conditions with geographic data through the AI-generated relationship graph. For example, they identified that flood claims were disproportionately high in Texas during specific seasons. Similarly, the system helped connect fraudulent medical claims to specific healthcare providers across multiple regions. This insight drove more strategic decision-making, allowing for better allocation of resources, fraud prevention efforts, and faster claim processing times.​

By digitizing over two decades of documentation, the AI solution not only made the data more accessible but also enabled the company to uncover trends and correlations that were previously impossible to detect manually. The solution provided a visual representation of complex relationships, empowering the company to optimize their operations and refine their strategies in a competitive insurance landscape.​