LLMs in Insurance: Practical Applications, Benchmarks, Deployment, and Governance

Large Language Models in Insurance: What Works Today and How to Deploy Safely

Generative AI has pushed large language models (LLMs) into daily business use. These systems are trained on massive text corpora and can write, summarize, translate, answer questions, and even generate code. For insurers and actuarial teams, they offer speed on well-defined tasks while keeping humans in the loop for judgment.

LLMs are strongest at language-heavy workflows. They quickly process long documents, create first drafts, and standardize outputs. They are not a substitute for actuarial analysis or decision-making, but they can remove a lot of friction from routine work.

Where LLMs help today in insurance

• Coding assistance: Code generation, refactoring, and automated documentation.
• Digital assistant: Email drafting, document creation, note taking, and meeting summaries.
• Data summarization and categorization: Claims notes, submissions, reinsurance treaties, medical underwriting files, and call or meeting transcripts.
• Testing and model validation assistance: Generating test cases, drafting testing documentation, review and validation support.
• Other applications: Translation, research source attribution, and claims system integration support.

Expert panels across actuarial practice areas agree: current tools can boost productivity but do not replace actuarial judgment. Adoption will become expected. Data privacy, security, compliance, and ethics must lead the rollout, with tight coordination across actuarial, IT, legal, and risk.

Picking the right model for the job

• Foundational models: General-purpose; no task-specific tuning.
• Instruct models: Tuned for following directions and task completion.
• Code models: Specialized for understanding and generating code.
• Multimodal models: Work across text, images, and audio.

Bigger isn't always better. Balance accuracy with latency, budget, scale, and risk controls. Test before committing.

• Model size vs. need: Simple tasks with quick responses → smaller models. Complex reasoning → larger models and more compute.
• Task performance: Evaluate on the data and formats your team actually uses.
• Context window: Ensure the model can handle long treaties, filings, or claim files in one pass.
• Cost vs. performance: Measure quality gains per dollar and per second of latency.

Useful public benchmarks

• MMLU (Massive Multitask Language Understanding): ~16,000 multiple-choice questions across topics from math to law.
• GPQA (Google-Proof Q&A): 448 expert-written questions in biology, physics, chemistry; probes expert-level knowledge.
• MATH (Mathematics Aptitude Test of Heuristics): 12,500 competition problems that require reasoning.
• HumanEval: Tests code-writing accuracy on 164 programming tasks.
• DROP (Discrete Reasoning Over Paragraphs): Evaluates reading comprehension and information extraction.

The gold standard is your own benchmark. Build a small, anonymized, task-specific test set that mirrors production work. Track performance over time and across model updates.

Deployment: API first, with guardrails

• API vs. self-hosting: APIs are fastest to pilot and often more cost-effective. Self-hosting gives more control but needs engineering capacity.
• Security and privacy: Require data encryption, retention controls, regional hosting options, and vendor attestations.
• Cloud over on-prem (for most): Faster to launch and scale. Engage cloud engineers and software developers for production setups.
• Access control and logging: SSO, least-privilege access, prompt/output logging, and change management.

Risk, ethics, and governance

• Privacy and protection: Meet data protection laws and company standards; restrict PII/PHI exposure.
• Risk and compliance: Regular human review of outputs; document controls; audit trails.
• Technology and reliability: Validate model capabilities, uptime, fallbacks, and support SLAs.
• Bias, fairness, discrimination: Test for and mitigate disparate impacts.
• Transparency and explainability: Document model selection, prompts, context sources, and usage policies.
• Accountability and responsibility: Assign clear owners for decisions, monitoring, and incident response.

Helpful frameworks:

• UNESCO's Recommendation on the Ethics of Artificial Intelligence
• NAIC Principles on Artificial Intelligence

Practical rollout checklist

• Pick one workflow with clear ROI (e.g., claims note summarization) and define success metrics.
• Create a redacted test set and baseline it with current process time/quality.
• Pilot with an API, add prompt templates, and enforce data handling rules.
• Measure accuracy, latency, and cost; compare to baseline; iterate.
• Codify review steps, exceptions, and escalation paths before scaling.

SOA resources for actuarial teams

• Operationalizing LLMs: A Guide for Actuaries - a practical deployment guide.
• AI Research landing page - reports and tools for actuarial use cases.
• Actuarial Intelligence Bulletin - monthly updates on tech and AI research.

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