Blog

AI, insurance and the business of pricing

Articles on AI in insurance pricing, distribution and regulation, written by the team that builds, launches and manages pricing for insurers and MGAs.

ComparisonsAI in pricingAI in insuranceRegulationDistributionEngineering
ComparisonsFeatured

Ptally compared: established pricing platforms and new AI entrants

How Ptally compares with established insurance pricing and rating platforms and with newer AI-led options, on upfront cost, implementation, use of AI, model readability and time to launch.

Ptally Pricing Team5 min read
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AI in pricing5 min read

Building insurance pricing models from US rate filings

How US rate and rule filings can be turned into working, tested pricing models: what a filing contains, where to find it, how AI speeds up extraction, and the limits to plan for.

Ptally Pricing Team
AI in pricing3 min read

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.

Ptally Pricing Team
AI in insurance3 min read

AI in insurance: where it delivers value today

A practical guide to where AI creates measurable value across insurance, from pricing and underwriting to distribution, claims and operations, and what successful projects have in common.

Ptally Pricing Team
Regulation3 min read

The benefits of AI in regulated industries, and why explainability comes first

How insurers and other regulated firms can benefit from AI while meeting expectations on fairness, transparency and accountability from regulators such as the FCA, NAIC and EU.

Ptally Regulation and Risk
Distribution3 min read

AI for insurance distribution: pricing in every channel

How AI is changing insurance distribution, from aggregators and embedded insurance to conversational quoting and AI agents, and why a real-time, explainable pricing engine is central to all of them.

Ptally Distribution
Engineering2 min read

Why insurance pricing models should be human readable, especially when AI writes them

As AI drafts more insurance pricing logic, the format of the model becomes a control. This article explains why declarative, readable pricing models are faster to review and audit than generated code.

Ptally Engineering

More articles

14
Every rate change needs a test suite: regression testing for insurance pricingPtally Engineering · · AI in pricing
How much does an insurance rating engine cost? Licences, implementation and pay as you writePtally Pricing Team · · Comparisons
Using Claude and ChatGPT to build insurance pricing models: what works and what doesn'tPtally Engineering · · Engineering
The EU AI Act and insurance pricing: what high-risk means in practicePtally Regulation and Risk · · Regulation
How to price pet insurance: rating factors and a worked examplePtally Pricing Team · · AI in pricing
Launching an MGA: getting your first product priced and livePtally Distribution · · Distribution
Consumer Duty and pricing models: how to evidence fair valuePtally Regulation and Risk · · Regulation
What to look for in an insurance pricing platform: a buyer's checklistPtally Pricing Team · · Comparisons
AI agents will buy insurance. Is your pricing ready?Ptally Distribution · · Distribution
Rate changes without the risk: a step-by-step process for pricing teamsPtally Pricing Team · · AI in pricing
The NAIC AI model bulletin explained for insurers and MGAsPtally Regulation and Risk · · Regulation
Replacing a spreadsheet rater: when to move and how to do it safelyPtally Engineering · · Engineering
Pricing for price comparison websites: latency, volume and fair pricing rulesPtally Distribution · · Distribution
Human in the loop: designing AI approval workflows for regulated pricingPtally Regulation and Risk · · Regulation
Guides

How to use the Ptally platform

Step-by-step guides with screenshots, from pricing your first quote to approving a rate change.

01Getting around Ptally: projects, versions and quotesA tour of the Ptally platform: the projects list, a project's page, a version's model, steps and tests, and where to find quotes, the API and MCP.2 min read02Price a quote: dry runs and live quotesHow to price a quote from a project or version page in Ptally, the difference between a dry run and a live quote, and how to copy the request as a curl command.2 min read03Run and read testsHow to run a version's test suite in Ptally, read the results, open a test to see its inputs and expected result, and add tests to a draft.2 min read04Edit a model in a draft versionHow to make changes to a pricing model in Ptally: create a draft version, edit a step or rate table, upload a model or CSV, and summarise the change for reviewers.2 min read05Approve and publish a versionHow a Ptally version moves from draft to approved to published, what each status means for live quotes, and where to see the version history.1 min read06Analyse quotesHow to use the Ptally quotes page: volume, latency, result and validity charts, filtering by project and version, searching quotes and opening a quote's full detail.1 min read07Manage users and API keysHow to add users, understand roles, find and rotate your API keys, and read the audit log in Ptally settings.1 min read08Connect an AI client and find the API referenceHow to use the MCP and API pages inside Ptally to connect Claude, ChatGPT or Cursor, and to find every API endpoint with examples.1 min read

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