When generative AI first hit insurance, many carriers did the same thing: push claims work to GPT and see what happens. Summaries, emails, triage notes, even denial letters. It proved the value, but cracks showed fast.
Ask GPT to reason over dense policy language, and hallucinations stop being funny. They turn into compliance risk. That is why more insurers now turn to custom insurance software development and treat small language models as the main engine for everyday claims.
GPT vs Small Language Models In Insurer Terms
Large models like GPT are generalists: big, expensive, trained on broad internet text. They shine at flexible reasoning and creative drafting, but are overkill for many routine tasks.
Small language models are compact specialists. They have fewer parameters, a narrow scope, and are fine-tuned on your policies, guidelines, and historical claims. They do not try to “do everything”. They read your documents, follow your rules, and respond fast inside your environment.
The practical trade-off:
- Big models: Broad skills and deep reasoning. Higher cost, higher latency, harder privacy story.
- Small models: Cheaper, faster, easier to run where your data lives. Limited to clear, designed tasks.
In claims, the sweet spot is using SLMs for the bulk of repetitive work and reserving GPT for cases where broad reasoning makes sense.
Reason 1: Claims-Scale Economics – SLMs Make Unit Cost Predictable
Claims operations generate millions of small language tasks: notes, comments, coverage checks, and status updates. At that scale, even a few cents per GPT call quickly becomes uncomfortable.
Small language models change the math. Because they are compact, they are much cheaper to run and can live on your own GPU or CPU estate. You are not paying frontier prices for work that mostly looks like classification, extraction, or short drafting.
That shift lets you push AI into low-value but high-volume parts of the journey. Instead of rationing usage, you can afford to automate the boring 80%, not just a handful of “hero” use cases. Unit cost becomes a predictable line in your operations budget.
Reason 2: Data Privacy And Auditability Regulators Accept
Claims data is some of the most sensitive information a carrier touches: medical details, accident photos, police reports, legal letters, full identity profiles. Regulators expect clarity on where that data lives and how decisions are made.
General cloud endpoints for GPT add another surface. Even with good controls, cross-border flows and third-party processors complicate the story.
Small models let you bring the model to the data. An SLM can run in your VPC, in your region, on infrastructure you already govern. Raw claims stay inside.
You get:
- Clean data residency: “This never leaves country X” enforced at the infra level.
- Stronger audit story: Full control over logs, retention, and access. Clear evidence of what the model saw and produced.
- Regulator-friendly architecture: Model artefacts managed like any other regulated system.
Instead of explaining where calls go, you show that data stays and models move.
Reason 3: Policy-Aware Accuracy – Fewer Risky Hallucinations
General models are great at language, but do not know your current products. They have no native concept of this year’s motor wording in a specific market or the exclusion that your regulator forced you to add last quarter.
Ask a general model to explain coverage or draft a denial letter, and it may improvise: referencing a clause from some other product or paraphrasing in a way that shifts the meaning. That is not acceptable in claims.
Small language models can be trained or fine-tuned on your own corpus:
- Current and past policy wordings
- Claims handling playbooks
- Historical decisions and approved letters
They learn your structure and vocabulary. Terms like “total loss” or “under-insurance” become precise concepts, not guesses.
In practice, you see fewer invented clauses and fewer “creative” explanations. When products change, you can retrain and re-approve the model like any other controlled component, with model risk and legal teams in the loop.
Reason 4: Latency and UX – Real-Time Help For Adjusters And Customers
Much of the claims work is live:
- A policyholder calling right after an accident.
- An adjuster at the scene is dictating notes.
- A contact centre agent is updating details while someone waits on the line.
In those moments, a few seconds of delay feels long. Ten seconds, and people fall back to manual work.
Small language models shine because they are fast and close to the action. They can run on contact centre servers, in regional data centres, or even on device-class hardware. Responses feel instant in calls and chats.
That carries practical patterns:
- A voice layer that listens to the caller, uses an SLM to detect intent and entities, and updates fields while the agent talks.
- An adjuster app that drafts damage descriptions from photos and short dictation.
- A chatbot that answers “what is happening with my claim?” in clear language without long pauses.
Fast, local models make AI feel like a help, not a blocker, which is exactly what overworked claims teams need.
Reason 5: Hybrid Architectures – SLMs Do The Grunt Work, GPT Handles Edge Cases
The most effective carriers build an AI assembly line instead of betting on one model.
In that line, SLMs handle the repetitive, narrow tasks:
- Classify incoming items (emails, notes, document types).
- Extract key fields (dates, amounts, parties, locations).
- Tag and route cases into fast-track, standard, or specialist queues.
- Draft short responses and status updates.
A GPT-class model only appears when a case looks ambiguous, high-value, or litigated. It helps senior handlers with deep summarisation, scenario comparison, and the suggestion of options.
A simple pattern looks like this:
- SLM 1 → classify
- SLM 2 → extract
- SLM 3 → score and route
- GPT → support for the rare, messy cases
This keeps costs and data exposure under control and makes behaviour easier to explain. Each small model has a clear job. The big one is a specialist on call instead of a jack-of-all-trades.
Implementation Checklist
To move from “we tried GPT” to “we run SLM-first claims”, start with journeys, not models:
Map claims micro-journeys
- Break down FNOL, triage, investigation, settlement, subrogation.
- Mark steps that are high-volume, text-heavy, and rule-driven.
Define your privacy boundary
- Decide which data must never leave your environment.
- Decide which tasks, if any, can safely use external LLM APIs.
Pick 3–5 narrow SLM use cases
- Good starters: document classification, entity extraction, simple routing, short status messages.
Put governance in place
- Log inputs, outputs, and key decisions.
- Add human review for high-risk actions.
- Monitor for drift and retrain on fresh data.
Then add GPT where it truly helps
- Use it as an escalation tool for rare, complex cases, not as the default engine.