Best AI for Actuaries

Actuaries are among the more unusual professionals in financial services: deeply quantitative, professionally certified, and yet responsible for written outputs that communicate technical analysis to audiences that range from fellow actuaries to state insurance commissioners to board members who want to understand what the reserve adequacy review actually means.

That writing work is substantial. Model documentation, assumption memos, regulatory filing narratives, ASOP compliance write-ups, peer review findings, and management reports all require clear technical writing that accurately represents complex quantitative work. Actuaries are trained to do the quantitative work; the writing is a time cost that comes with the role.

AI tools are genuinely useful for this writing work in ways that don't require compromising the professional integrity of the actuarial work itself. This guide covers three tools that fit real actuarial workflows, with specific notes on where each one helps most.

What AI tools can and can't do in actuarial work

Being specific about the boundary here matters.

AI can help with: Drafting the written sections of model documentation, regulatory memos, and assumption papers. Explaining actuarial concepts in plain language for non-actuarial audiences. Writing Python or R code for common actuarial calculations. Researching regulatory guidance and published standards from external sources. Structuring the narrative of complex technical documents.

AI cannot replace: The actuarial judgment behind the assumptions, the professional sign-off on certified work product, the validation that the model is producing actuarially appropriate results, and the professional responsibility that comes with credentialed actuarial work. No AI output is a substitute for the actuary's professional responsibility to review and certify their own work.

With that boundary clear: here's where the tools help.

1. Claude (claude.ai)

Claude is the right tool for actuaries who want AI assistance with the technical writing that comes with model documentation, regulatory correspondence, and assumption justification.

For model documentation, the value is in drafting the sections that have consistent structure but variable content. Every model documentation package has a methodology section that explains how the model works, a limitations section that describes what the model doesn't do, and an assumption section that documents the key inputs and their justification. These sections need to be accurate, clear, and complete. Writing them well takes time, even when the actuary knows the model thoroughly. Claude produces solid first drafts from structured notes and prompts, which the actuary then validates, corrects, and refines to accurately represent the model.

For ASOP compliance write-ups, Claude is familiar with the Actuarial Standards of Practice framework and can help structure compliance narratives that address the relevant disclosure requirements. The specific compliance assessment is still the actuary's responsibility, but the writing that explains how a methodology complies with a relevant ASOP goes faster with AI assistance.

For assumption memos and basis documentation, the writing task is explaining why specific mortality, lapse, expense, or investment assumptions were selected, what sources support them, and how they compare to industry benchmarks. Claude drafts this structure well when given the key facts, though all source citations and industry comparisons need verification against the actual data.

For management presentations and board reports, Claude translates technical actuarial analysis into plain language effectively. The translation task, taking a reserve adequacy finding and writing it in terms a non-actuary board member can interpret, is something Claude does better than most technical professionals expect.

For regulatory correspondence, Claude drafts response letters, information request responses, and filing narratives professionally. The language and structure of regulatory correspondence is something Claude handles well for the written communication part; the actuarial substance is still the actuary's responsibility.

The data caveat: standard Claude.ai consumer plans are not appropriate for uploading proprietary policyholder data or non-public company financial information. Use Claude for writing and drafting based on publicly available information and your own summarized analysis.

Best for: Model documentation, ASOP compliance write-ups, assumption memos, regulatory correspondence, and management reporting. Pricing: Free tier available; Claude Pro at $20/month.

2. Claude Code

Claude Code is the right tool for actuaries who write Python or R code for modeling work and want AI coding assistance.

Actuarial modeling has been moving toward Python and R for several years, and actuaries who are building models, running analyses, and automating calculations in those languages benefit from AI coding assistance in the same way any quantitative professional does.

The specific actuarial applications where Claude Code helps: implementing mortality table calculations and survival functions. Building reserve triangles and development methods in pandas or data.table. Writing present value calculation functions for annuity or insurance product pricing. Implementing GLM or regression models for experience studies. Automating the report generation workflow that takes model outputs and produces formatted tables.

Claude Code understands actuarial concepts well enough to implement them correctly more often than a general-purpose coding agent would. It knows what a select and ultimate mortality table looks like, it understands why you interpolate rather than extrapolate in mortality studies, and it knows the standard approaches for development pattern selection in reserve triangles. That domain knowledge in the coding layer is the difference between getting a good implementation on the first try and spending time correcting a mechanically correct but actuarially wrong result.

For actuaries building internal models, Claude Code is also useful for the data pipeline work: loading and cleaning policyholder data exports, transforming data into the format the model needs, and automating the reconciliation checks that confirm the data loaded correctly.

Best for: Python and R implementation for actuarial calculations, model code, data pipelines, and report automation. Pricing: Included with Claude Pro at $20/month; API usage billed by token.

3. Perplexity

Perplexity covers the external research that comes up in actuarial work: current regulatory guidance, published mortality tables, industry experience studies, NAIC guidance updates, and any external information that might have changed recently.

For actuaries doing valuation work, staying current on regulatory changes (AG 49, NAIC model laws, principle-based reserving updates, state filing requirement changes) is a constant requirement. Perplexity's real-time search finds current regulatory publications and shows you the sources, which is important when you need to verify that you're working with current guidance rather than something that was superseded.

For assumption-setting, Perplexity helps locate published industry experience studies, Society of Actuaries research, and LIMRA publications on mortality, lapse, and expense trends. Finding the most recent industry experience data is a research task that Perplexity handles faster than manual searching across multiple industry association websites.

For research on emerging actuarial topics, climate risk modeling, long COVID mortality impacts, behavioral lapse patterns, Perplexity finds recent academic and practitioner publications quickly with citations you can follow to the primary source.

At $20/month for Perplexity Pro, it's worth having for any actuary doing significant regulatory research or assumption justification work.

Best for: Current regulatory guidance, published mortality and experience studies, industry research, and assumption justification sources. Pricing: Free tier available; Perplexity Pro at $20/month.

Putting these tools to work in an actuarial workflow

Here's how the three tools fit into a concrete actuarial workflow, using the annual model documentation update as an example.

Start with Perplexity to research any regulatory guidance updates since the last documentation cycle. Are there new ASOP pronouncements? New NAIC guidance relevant to the model's application? Updated industry tables that should inform the assumption review? Getting current on the regulatory and industry landscape takes thirty minutes with Perplexity and would take significantly longer manually.

Build the model updates in Python or R using Claude Code. Claude Code handles the calculation logic and data pipeline work for common actuarial calculations without requiring extensive prompt engineering.

Draft the updated model documentation using Claude. Provide Claude with structured notes: what changed, why, what the key assumptions are and their justification, what the model does and doesn't cover. Claude produces the written sections from those inputs. You validate every technical claim, add the specific numbers and model results from your actual analysis, and review the documentation against the relevant ASOPs.

The actuary's sign-off on everything is unchanged. The writing just takes less time.

Frequently asked questions

Can AI tools help actuaries prepare for credentialing exams?

Claude is genuinely useful as a study companion for actuarial exam preparation. It can explain probability and statistics concepts, work through exam-style problems, and help identify where your understanding has gaps. It's not a substitute for practice exams and proper exam preparation materials, but as a concept explanation tool it's useful. Don't rely on it for final answer validation on complex exam questions; verify against published solutions.

Is AI useful for peer review documentation?

Yes, for the write-up of peer review findings. Documenting what was reviewed, what the peer reviewer checked, any findings that were identified, and the resolution of those findings is writing work that Claude handles well. The peer review itself requires a credentialed actuary reviewing the work; AI assists with documenting the process and findings.

What about AI tools for stochastic modeling documentation?

Stochastic model documentation is more complex than deterministic model documentation because it requires explaining the scenario generation methodology, the convergence criteria, and the interpretation of percentile-based results. Claude handles this documentation well when given clear inputs about the methodology. For the actual model code, Claude Code can implement common stochastic scenario generators and analysis routines in Python or R.