Artificial intelligence has transformed our industry. In the past year, AI has moved beyond proving its potential to streamline operations, becoming a force that improves and enhances performance in areas from claims processing to underwriting to customer service.
Many of these advances are powered by fine-tuned large language models (LLMs), which can fluently understand and generate natural language. However, to truly deliver value in insurance, these models need more than general language skills—they need to understand the insurance industry’s specialized vocabulary, rules, and workflows.
And this is where fine-tuning comes in.
Fine-tuning involves “teaching” a pre-trained LLM that already understands general language patterns and training it further on a smaller, specialized dataset. Fine-tuned large language models have undergone this additional training round to become adept at interpreting claims notes, understanding policy terms, flagging compliance issues, and other insurance-related work.
Think of it this way: a pre-trained LLM is like a new hire who’s an expert communicator, but unfamiliar with insurance. Fine-tuning is the onboarding process for teaching them your company’s terminology, policies, and best practices so that they can perform at their best in your environment.
Insurance has specialized terminology, acronyms/abbreviations, and highly nuanced regulatory language. A general-purpose LLM lacking contextual knowledge might misunderstand or misinterpret standard insurance terms (e.g., COI, “admitted/non-admitted”, “retention”), necessitating human rework of data and thus sapping operational efficiency. Insurance fine-tuned models learn to recognize and accurately process our industry’s unique lexicon.
Accuracy is critical whether it’s automating claims triage, supporting underwriters with risk analysis, or answering customer queries. Fine-tuned models deliver more precise results because the best models for insurance are trained on a vast corpus of real examples from insurance workflows, reducing the risk of mistakes and the need for manual corrections.
While many insurance practices are standardized, every insurer has its own products, processes, and documentation styles. Fine-tuning allows LLMs to be customized to reflect an organization’s unique way of working—specific policy language, claims procedures, or compliance requirements—without starting from scratch.
Insurance companies have access to vast amounts of proprietary data, from claims histories to customer communications. Fine-tuning enables you to use this data, teaching your AI to recognize patterns, flag anomalies, or generate insights that are directly relevant to your business.
“Annotation” is a critical step in improving AI-powered document processing that provides models with human-reviewed examples, including claims forms, transcripts of customer interactions, geospatial data, and other structured and unstructured documents.
Benefits of annotation include:
For insurance leaders in IT, underwriting, and claims, the path to fine-tuning starts with identifying key workflows or pain points where AI can make a difference. The next step is gathering high-quality, representative data—such as claims, policies, and customer interactions—that can be used to teach the model. Finally, work with your organization’s AI experts to fine-tune and evaluate the model’s performance, ensuring it meets organizational needs before deployment.
Agentic AI refers to systems that can autonomously pursue complex goals, make decisions, and adapt actions based on changing circumstances. Fine-tuning is foundational to agentic AI. To operate effectively and efficiently, an AI agent must deeply understand insurance-specific language, workflows, and regulatory requirements.
Fine-tuning large language models with insurance data provides this critical knowledge, allowing the AI to reason, plan, and execute multi-step processes such as claims adjudication or underwriting reviews. By grounding the model in the realities of insurance, fine-tuning plays a critical role in transforming general LLMs into intelligent “agents” for insurance underwriting, claims management, and customer service.
Fine-tuning is the critical step that transforms large language models from generalists into insurance specialists. By teaching AI to understand our industry’s language and workflows, fine-tuning unlocks enhanced accuracy, efficiency, and value from your AI investments. As insurance’s tech transformation powers on, fine-tuned models provide an essential link to improving operational efficiency and delivering better outcomes for insurers and policyholders.
Curious how insurers are putting Roots to work? Check out our case studies to see insurance-specific AI in action.