AI in insurance pricing: from rating guide to live model in days
How AI is changing the way insurers and MGAs build, test and launch pricing models, and why the best results come from pairing AI with experienced pricing consultants.
Most insurance products are not delayed by pricing design. They are delayed by pricing implementation. The actuarial work on base rates, relativities and underwriting rules is often finished months before the first quote can be served. The time between a signed-off rating guide and a live, tested, integrated model is where most of the cost and delay sits.
This is the part of the process where AI is having the biggest effect.
Why implementation takes so long
A rating guide usually arrives as a PDF, a spreadsheet or a regulatory filing. Someone has to turn it into software. That means typing in every table, encoding every rule, putting the calculations in the right order and then showing that the results match the actuary's worked examples to the penny.
The person doing this is usually a developer who is not a pricing specialist, working from a document written by a pricing specialist who is not a developer. Every unclear point leads to a meeting, and every rate change starts the process again. As a result, many carriers and MGAs make rate changes once a quarter when they would prefer to make them far more often.
What AI can do
Large language models are good at reading documents, structuring information and converting it from one format to another. That covers much of the work that used to take weeks:
- Extraction. Pulling rate tables, factor definitions and rules out of PDFs, spreadsheets and filings into structured data.
- Drafting. Turning a written algorithm, such as "base rate by territory, multiplied by an age factor and a construction factor, plus a policy fee", into a working model.
- Validation. Checking a model against a schema and against the rating guide's worked examples, and describing any mismatch in plain English.
- Iteration. Applying a rate change to a draft, re-running the tests and summarising what changed.
With this approach, a first working draft of a pricing model can be ready in hours. A draft still needs to be checked before it is launched.
Why AI on its own is not enough
Pricing is regulated and mistakes are expensive. A model that is right 99% of the time is still wrong. If an AI misreads a footnote in a rating guide, it can misprice a whole book of business. If nobody can read the model the AI produced, nobody can approve it.
The approach that works in practice splits the work:
- AI handles extraction, drafting and a first round of validation.
- Pricing consultants review every table and rule against the source document and make the decisions the document leaves open.
- A test suite built from the actuary's worked examples shows that the model prices correctly to the penny.
- A person approves and publishes the model. The AI does not.
The model format matters
For this to work, both an AI and a person need to be able to read the pricing model. Generated program code is quick to produce but slow to review. A model written as structured configuration, with named inputs, rate tables stored as rows and formulas written out in full, can be drafted by an AI agent and reviewed by an underwriter on the same day.
Ptally models are built this way. Each model is a structured JSON file with its tests stored alongside it. The AI works on that file, the reviewer checks it and the rating engine runs it in production.
What this means for carriers and MGAs
When implementation is no longer the slowest step, pricing teams can work differently:
- More products. New lines, territories and trial propositions become practical when each one no longer needs a six-month build.
- More frequent rate changes. Responding to claims inflation or a competitor's move can take days, with every change tested before release.
- More time on pricing. Actuaries spend their time on rates and strategy rather than on tracking down implementation errors.
AI does not replace the pricing actuary. It removes the slowest and least valuable part of their work, and it makes it realistic to update pricing continuously rather than once a year.
Getting started
A rating guide, a spreadsheet rater or a filing is enough to begin. Ptally's consultants use AI to build the model, test it against your examples and launch it, with no implementation fee. Talk to our team to see your product running as a live model.