How AI is Revolutionizing Insurance Operations

Large language models (LLMs) are driving significant change in the commercial insurance industry by streamlining risk processing and enhancing profitability without adding operational costs. These models help insurers onboard, renew, and service risks at nearly zero marginal cost, with consistent control over risk selection. By improving scalability and accuracy in risk evaluation, LLMs enable insurers to write more risks and adjudicate more claims with the same resources, thus supporting profitable growth.

LLMs are trained on vast data sets, allowing them to understand and generate content to perform a variety of tasks. When tailored to specific domains like commercial insurance, they solve complex problems that unlock significant value. These models digitize and standardize heterogeneous and unstructured risk data, making risk decisions more efficient across the value chain. They are particularly effective in underwriting and claims processing, where they can handle tasks from document classification to auto-adjudication of low-complexity claims, thus enhancing customer service and operational efficiency.

The architecture of LLMs, based on multiple layers of neural networks, allows them to generate both structured and unstructured content effectively. Fine-tuning these models, including techniques like reinforcement learning with human feedback, helps remove biases and factual errors, making them even more reliable for insurance applications. By transforming dispersed risk data into a unified format, LLMs enable insurers to maintain their unique risk perspectives while achieving high levels of efficiency.