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🤖 '''Large language model (LLM)''' is a type of [[Definition:Artificial intelligence (AI) | artificial intelligence]] system, trained on vast text corpora, that can generate, summarize, classify, and reason over natural language — and it is rapidly reshaping how [[Definition:Insurance carrier | insurers]], [[Definition:Managing general agent (MGA) | MGAs]], and [[Definition:Insurtech | insurtechs]] handle everything from [[Definition:Underwriting | underwriting]] submissions to [[Definition:Claims management | claims]] correspondence. Built on deep neural network architectures (most notably the transformer), LLMs learn statistical patterns in language that allow them to draft policy wordings, extract data from unstructured [[Definition:Submission | submissions]], answer policyholder questions through conversational interfaces, and flag anomalies in [[Definition:Claims adjuster | adjuster]] notes. Prominent examples include OpenAI's GPT series, Anthropic's Claude, Google's Gemini, and open-source alternatives increasingly adopted by insurance technology teams.

⚙️ In practice, insurers deploy LLMs across the value chain. On the [[Definition:Underwriting | underwriting]] desk, an LLM can ingest a multi-page [[Definition:Statement of values | statement of values]] or [[Definition:Submission | broker submission]] and extract key risk characteristics in seconds, dramatically cutting triage time. In [[Definition:Claims management | claims]] operations, models summarize medical records, draft reserve recommendations, or detect potential [[Definition:Insurance fraud | fraud]] indicators in narrative descriptions. Customer-facing [[Definition:Chatbot | chatbots]] powered by LLMs handle [[Definition:First notice of loss (FNOL) | first notice of loss]] intake and routine policy inquiries, freeing human staff for complex interactions. Fine-tuning on insurance-specific data — policy forms, regulatory filings, court opinions — improves domain accuracy, and [[Definition:Retrieval-augmented generation (RAG) | retrieval-augmented generation]] techniques ground the model's outputs in an insurer's proprietary knowledge base, reducing hallucination risk.

🔮 The strategic implications for the industry are profound but come paired with serious governance considerations. LLMs can accelerate speed-to-quote, improve [[Definition:Straight-through processing (STP) | straight-through processing]] rates, and unlock insights buried in decades of unstructured files — efficiencies that translate directly into competitive advantage. However, [[Definition:Insurance regulator | regulators]] are scrutinizing AI-driven decisions for bias, transparency, and [[Definition:Explainability | explainability]], particularly where model outputs influence [[Definition:Rating | rating]], [[Definition:Claims settlement | claims settlement]], or coverage determinations. Insurers adopting LLMs must invest in robust [[Definition:Model governance | model governance]] frameworks, human-in-the-loop review processes, and data privacy safeguards to ensure that the technology's benefits are realized without running afoul of regulatory expectations or eroding [[Definition:Policyholder | policyholder]] trust.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Predictive analytics]]
* [[Definition:Straight-through processing (STP)]]
* [[Definition:Optical character recognition (OCR)]]
* [[Definition:Insurtech]]
* [[Definition:Model governance]]
{{Div col end}}

Definition:Large language model (LLM) - Revision history

PlumBot: Bot: Creating new article from JSON