Large Language Models (LLMs)

What are large language models (LLMs)? #

Large language models, also known as LLMs, are a category of deep learning models trained on massive amounts of text data, which encompasses books, websites, academic papers, and more, that are capable of understanding, generating, and reasoning with natural language at scale. Built on a neural network architecture called a transformer, LLMs learn statistical patterns and relationships between words and phrases, enabling them to perform a broad range of language tasks, from answering questions and summarizing documents to drafting content and writing code. Unlike traditional software that follows rigid, pre-programmed rules, an LLM improves its performance through exposure to data, allowing it to handle the nuance, context, and ambiguity inherent in human language. Well-known examples of LLMs include OpenAI's GPT-4, Google's Gemini, Meta's Llama, and Anthropic's Claude.

How do large language models work? #

LLMs work by processing text as sequences of smaller units called tokens (words, subwords, or characters) and learning the statistical relationships between them. During training, the model passes these tokens through a transformer network equipped with a self-attention mechanism, which allows it to weigh the relevance of each word in relation to every other word in a sequence, capturing long-range context and meaning. Through billions of training examples, the model adjusts its internal parameters to improve its predictions. Once trained, an LLM generates output through a process called inference: given a prompt, it predicts the most probable next token, one at a time, until a complete response is formed. The model does not retrieve stored answers, but constructs responses based on the patterns it learned during training.

How are LLMs trained? #

Training an LLM happens in three main phases. The first is pre-training, where the model is exposed to vast datasets of text and learns general language patterns like grammar, facts, context, and reasoning, through self-supervised learning, without the need for manually labelled data. The second phase is fine-tuning, where the pre-trained model is further trained on a smaller, task-specific or domain-specific dataset to improve its performance for specific applications, such as answering legal questions or processing insurance documents. The third phase is reinforcement learning from human feedback (RLHF), in which human evaluators rank the model's outputs and those preferences are used to further refine the model's behaviour, improving its accuracy, helpfulness, and safety. Together, these phases transform a general-purpose language model into a reliable, task-ready system.

Applications of LLMs in insurance and wealth #

LLMs are increasingly being adopted across the insurance and wealth management industries to automate complex language tasks, improve operational efficiency, and enhance client experiences. Key applications include:

• Underwriting support: LLMs can analyze applicant data, medical records, and risk documentation to help underwriters assess risk profiles faster and more consistently.
• Claims processing: LLMs can extract and interpret information from claim submissions, loss reports, and supporting documents, accelerating adjudication, and reducing manual review time.
• Policy document analysis: LLMs can read and summarize lengthy policy contracts, flagging key terms, exclusions, and coverage gaps for both insurers and policyholders.
• Fraud detection: LLMs can identify suspicious patterns in claims narratives and communications that may indicate fraudulent activity, complementing traditional rule-based detection systems.
• Regulatory compliance: LLMs can monitor internal documents and communications against evolving regulatory requirements, helping insurers stay compliant across multiple jurisdictions.
• Customer service automation: LLMs power intelligent chatbots and virtual agents that can handle policyholder inquiries, process requests, and escalate complex issues at scale and around the clock.
• Client onboarding: In wealth management, LLMs can streamline the onboarding process by extracting and validating information from identity documents, financial statements, and know-your-client (KYC) forms.
• Financial document summarization: LLMs can condense portfolio reports, earnings summaries, and market analyses into concise briefs, allowing advisors to focus on client strategy rather than document review.
• Advisor productivity tools: LLMs can draft personalized client communications, generate meeting summaries, and surface relevant insights from client history, freeing advisors to spend more time on high-value interactions.

Large language models vs. generative AI✦ ✦" aria-label="Direct link to Large language models vs. generative AI✦">#

LLMs and generative AI are closely related but not the same thing. Generative AI is the broader category that refers to any AI system capable of creating new content, whether text, images, audio, video, or code. LLMs are a specific subset of generative AI focused exclusively on understanding and generating human language. In other words, all LLMs are a form of generative AI, but not all generative AI is an LLM. An image generator like DALL-E, for instance, is generative AI but not a large language model.

Deep learning vs. large language models (LLMs) #

Deep learning is the foundational technology upon which LLMs are built. Deep learning refers to a broad family of machine learning techniques that use multi-layered neural networks to learn complex patterns from large datasets, spanning computer vision, speech recognition, recommendation systems, and more. LLMs are a specific application of deep learning, specifically leveraging a deep learning architecture called the transformer, and are focused entirely on language understanding and generation. Think of deep learning as the discipline and LLMs as one of its most advanced outputs.