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AI for Actuaries

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The shift

How AI is changing the Actuary role

In 2026, AI is handling more of the repetitive work in actuarial practice, from cleaning and reconciling large claims datasets to drafting documentation for reserving and pricing reviews. Language models help summarize regulatory updates and explain model assumptions to non-technical stakeholders, while coding assistants speed up the building and testing of pricing and capital models. The actuary's role is shifting toward reviewing AI output, validating assumptions, and owning the professional judgment behind the numbers.

What AI can take off your plate

  • Drafting first versions of technical memos, assumption logs, and review documentation
  • Writing and debugging code for reserving, pricing, and capital models
  • Cleaning, reconciling, and reshaping large claims and policy datasets
  • Summarizing regulatory updates and lengthy technical reports
  • Building routine dashboards and recurring exhibit calculations

What stays distinctly human

  • Setting and owning the professional judgment behind reserving and pricing assumptions
  • Signing off on results and taking responsibility under actuarial standards
  • Judging when a model is inappropriate for the business context
  • Communicating uncertainty and trade-offs honestly to boards and regulators
  • Weighing ethical and fairness implications of rating and underwriting decisions
Tools

Five AI tools for Actuarys

GitHub Copilot
An actuary uses it to write and debug R or Python code for reserving triangles, pricing models, and data pipelines faster.
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ChatGPT
An actuary uses it to draft technical memos, summarize regulatory documents, and explain assumptions in plain language for committees.
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Microsoft Copilot in Excel
An actuary uses it to build formulas, pivot summaries, and quick analyses across large policy and claims spreadsheets.
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Claude
An actuary uses it to review long actuarial reports or solvency regulations and extract the relevant requirements and changes.
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DataRobot
An actuary uses it to build and compare predictive models for lapse, mortality, or claims frequency without coding each one by hand.
Try it →
Prompts

Five prompts to try today

Paste these into Claude or ChatGPT and replace the bracketed parts with your own details.

1. Explain a reserving method
Explain the [chain ladder / Bornhuetter-Ferguson] reserving method to a non-actuarial audit committee. Cover the key assumptions, when it breaks down, and what limitations I should flag for [line of business].
2. Draft assumption documentation
Draft documentation for the following actuarial assumption: [assumption description]. Include the rationale, data sources used, sensitivity to change, and a section on key uncertainties. Keep the tone formal and suitable for a peer review.
3. Code a pricing calculation
Write [R / Python] code to calculate a risk premium for [product] using these rating factors: [list factors]. Include comments, handle missing values, and add basic validation checks on the output.
4. Summarize a regulation
Summarize the key requirements of [regulation or standard, e.g. IFRS 17 / Solvency II] that affect [reserving / capital / disclosure]. List the practical actions an actuarial team must take and any recent changes from prior versions.
5. Sense-check model results
Here are my model outputs for [metric] across [periods or segments]: [paste data]. Identify any results that look inconsistent or unexpected, suggest possible causes, and list checks I should run before signing off.
The playbook

Every AI play for Actuaries

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NotebookLM FREE  Google's free tool that answers only from sources you upload, with citations

1

Go to notebooklm.google.com, sign in with a free Google account, start a notebook, and upload the report PDF (or paste its text) as a source.

2

Ask it to pull the parts that matter to you, so the answer stays grounded in the document:

Summarize this report in 8 bullet points a busy [my role] needs to know. Then list the 3 decisions or risks it raises. Quote the exact sentence behind each point and cite the page.
3

Follow up on anything unclear instead of rereading:

Where does this report talk about [topic or number]? Give me the exact lines and the page.

You get the real content and its sources in minutes, instead of skimming and hoping you caught the important part.

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Your playbook
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How AI is changing your role
Where your work is heading.
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