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🧠 '''Large language model''' refers to a class of [[Definition:Artificial intelligence (AI) | artificial intelligence]] system trained on vast text corpora to understand, generate, and reason about natural language — and within the insurance industry, these models are rapidly reshaping how carriers, [[Definition:Managing general agent (MGA) | MGAs]], and [[Definition:Insurtech | insurtechs]] handle everything from [[Definition:Underwriting | underwriting]] triage to [[Definition:Claims management | claims]] processing. Built on transformer-based neural network architectures, large language models such as OpenAI's GPT series, Anthropic's Claude, and open-source alternatives like LLaMA can parse policy wordings, summarize [[Definition:Loss run | loss runs]], draft [[Definition:Subrogation | subrogation]] letters, and extract structured data from unstructured [[Definition:Submission | submissions]] — tasks that traditionally consumed enormous amounts of human effort across insurance value chains.

⚙️ In practice, insurers deploy large language models through several integration patterns. Some embed them into existing [[Definition:Policy administration system | policy administration systems]] or [[Definition:Claims management system | claims platforms]] via application programming interfaces, enabling underwriters or adjusters to query the model against internal data sets — for example, asking it to compare a new submission's risk profile against historical [[Definition:Bordereaux | bordereaux]] data. Others use fine-tuned models trained on proprietary [[Definition:Actuarial science | actuarial]] reports, regulatory filings, or [[Definition:Reinsurance | reinsurance]] contract language to perform domain-specific tasks with higher accuracy than a general-purpose model could achieve. [[Definition:Lloyd's of London | Lloyd's]] has explored how these tools can accelerate the placement process, while regulators in multiple jurisdictions — including the European Insurance and Occupational Pensions Authority and state departments of insurance in the United States — are examining how model outputs affect [[Definition:Underwriting guidelines | underwriting guidelines]], [[Definition:Fair discrimination | rating fairness]], and [[Definition:Regulatory compliance | compliance]] obligations. A key operational challenge is hallucination risk: the model may generate plausible but factually incorrect policy interpretations, making human oversight and robust validation workflows essential.

💡 The significance of large language models for insurance extends well beyond efficiency gains. They are beginning to alter competitive dynamics by lowering the barrier for smaller [[Definition:Managing general agent (MGA) | MGAs]] and [[Definition:Insurtech | insurtechs]] to build sophisticated underwriting and servicing capabilities without the large back-office teams that incumbents rely on. In [[Definition:Specialty insurance | specialty lines]] — where policy language is complex and bespoke — these models can accelerate quote turnaround times and reduce [[Definition:Expense ratio | expense ratios]]. At the same time, their use raises governance questions around data privacy, model explainability, and the potential for [[Definition:Algorithmic bias | algorithmic bias]] in coverage or pricing decisions, prompting industry bodies and regulators across the United States, Europe, and Asia to develop frameworks specific to AI adoption in financial services. As the technology matures, large language models are likely to become foundational infrastructure in insurance operations rather than a novelty.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Machine learning]]
* [[Definition:Natural language processing (NLP)]]
* [[Definition:Insurtech]]
* [[Definition:Optical character recognition (OCR)]]
* [[Definition:Algorithmic underwriting]]
{{Div col end}}

Definition:Large language model - Revision history

PlumBot: Bot: Creating new article from JSON