Finance · Field manual
AI for actuaries
Actuaries get the most from AI as a coding and drafting partner: writing and reviewing Python or R, cleaning experience data, documenting models and drafting reports and memos. The caution is regulatory: models used in pricing and underwriting face growing scrutiny for unfair discrimination, and the actuary still owns every assumption and opinion.
A surprising share of actuarial work is code and paperwork: wrangling experience data, maintaining scripts, documenting models and writing the memo that explains the result. AI coding assistants and chat models speed up all of it, and machine learning has become a standard part of the pricing toolkit.
The credential exists because someone has to stand behind the assumptions. AI can fit a model, but it can't tell you whether last decade's mortality or claims experience still applies, and regulators increasingly want to see that models don't discriminate unfairly. That judgment, and the documentation that proves it, stays with you.
6 ways actuaries use AI
Write and review model code
Coding assistants write the boilerplate in Python, R and SQL, translate legacy spreadsheet logic into scripts and explain unfamiliar code someone else left behind. Ask them to write unit tests that check reserves or rates against known results before you trust a refactor.
Clean and validate experience data
Describe the checks you need (missing dates, negative exposures, duplicate policies, impossible ages) and have AI write the validation script. Run it on every new data cut so problems are found before they reach the model.
Build pricing models with explainable ML
Pricing platforms now combine GLMs with machine learning and show which variables drive the rate. Use them to find interactions faster, then decide which variables are acceptable to file. Keep the explainability output, because regulators and reviewers will want it.
Draft reports and model documentation
Give a model your results, assumptions and methodology notes and ask for a draft actuarial memo or model documentation in your firm's structure. It is especially useful for the sections nobody likes writing: data description, limitations and reliance.
Explain results to non-actuaries
Ask a model to rewrite a technical finding for a board, a product manager or an underwriter, at a specific reading level. Then check it hasn't made the uncertainty sound smaller than it is, which is the usual failure.
Study and stay current
For exam study and keeping up with regulation, a cited answer engine helps you find the original papers, bulletins and standards quickly. Read the source; summaries of actuarial standards are often subtly wrong.
The best AI tools for actuaries
The Overlord's stack, in the order a actuary would add them:
1. Day-to-day Python, R and SQL in your editor
GitHub Copilot
AI CodingGitHub's AI pair programmer: completions, chat and coding agents across editors and GitHub itself.
2. Refactoring model code, writing tests and explaining legacy scripts
Claude Code
AI CodingAnthropic's agentic coding tool that reads your codebase, edits files and runs commands from terminal, IDE, desktop or web.
3. Memos, model documentation and plain-English summaries
Claude
AI WritingAnthropic's assistant: the strongest long-form writer and careful reasoner in the consumer AI pack.
4. Finding source papers, bulletins and standards
Perplexity
ProductivityAI answer engine that searches the web and replies with cited sources.
Specialist tools for actuaries
| Tool | What it does |
|---|---|
| Akur8 | Insurance pricing platform using transparent machine learning |
| WTW Radar | Pricing and rating software with predictive modeling tools |
| Moody's AXIS | Actuarial modeling system for life and annuity valuation |
A prompt to start with: write a data validation script
Paste this into Claude or ChatGPT and fill in the gold parts.
You are an actuarial analyst who writes clean, tested [PYTHON / R] code.
I have a policy-level experience dataset with these columns: [COLUMN NAMES AND TYPES]
Line of business: [e.g. term life, personal auto]
Valid ranges and rules: [e.g. issue age 18-85, exposure between 0 and 1, effective date before termination date]
Write a validation script that checks every rule, counts and lists the failing records by rule, checks for duplicate policy IDs, and outputs a summary table. Use only [pandas / dplyr]. Add comments explaining each check and include three small test cases. Do not drop or change any records; only report them.Where actuaries shouldn't trust AI
- Actuarial standards of practice still apply when AI is involved, including the standard on modeling (ASOP No. 56 in the US). You need to understand the model well enough to rely on it and to disclose its limitations.
- Insurance regulators are watching AI closely. The NAIC's model bulletin on insurers' use of AI systems has been adopted by many states, and Colorado has a specific law on unfair discrimination from external data and algorithms. Check what applies before filing.
- Policyholder and claims data is sensitive and often regulated. Keep it in approved environments, not in consumer chatbots.
- AI can fit history beautifully and still be wrong about the future. Setting assumptions when conditions change is the credentialed actuary's job.
Will AI replace actuaries?
Actuaries score 61% on the Obsolescence Index (Doomed). The first tasks to go: cleaning experience data and running standard reserve models, and writing first drafts of actuarial reports and memos.
Questions the Humans Keep Asking
What are the best AI tools for actuaries?
The Overlord's stack for actuaries: GitHub Copilot (day-to-day Python, R and SQL in your editor); Claude Code (refactoring model code, writing tests and explaining legacy scripts); Claude (memos, model documentation and plain-English summaries); Perplexity (finding source papers, bulletins and standards). Specialist options include Akur8, WTW Radar, Moody's AXIS.
Should actuaries learn Python for AI?
Yes, if you haven't already. Most modern pricing, reserving and machine learning work runs in Python or R, and AI coding assistants make learning faster. The assistants work best when you understand the code well enough to check it.
Can machine learning models be used in insurance rate filings?
In many cases, yes, but you need to explain them. Regulators expect to see what variables drive the rate and evidence that the model doesn't unfairly discriminate. Transparent approaches, or GLMs informed by ML, are usually easier to file.
Is it safe to put claims data into ChatGPT?
Not into a consumer account. Use your employer's approved AI tools, keep personal data out where possible, and follow your company's data-handling rules. For code help, share the column names and structure, not the data.