AI for Actuarial Analyst
Actuarial analysts spend an estimated 10 hours a week pulling, reconciling and checking data, and another 6 hours on documentation and change logs, before a single exhibit goes to the reviewing actuary. The guides below show where AI can help with the checks, code, documentation, explanations and drafts around that work, from copy-paste prompts to a weekly digest that emails you what is due. The AI never produces your official numbers or picks an assumption. Your workbook, your model and the credentialed actuary who reviews your work stay in charge.
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Copy a prompt, paste into ChatGPT, Claude, or Gemini
Works with any free AI chatbot, no signup needed
A rewritten version of your request that asks for what an experienced professional would ask for.
Here's a task I'm about to do: [describe the task]. Don't do it yet. First, tell me how the best person in my field would approach it: what they would check that I haven't mentioned, what raw information they would want instead of a summary, and how they would prove the result is right. Then rewrite my request so it asks for that version.
View full prompt →Tip: - Check the result against something outside the chat: the official source, last period's actual figures, a colleague who knows the area, or a calculator. The AI saying its answer is right proves nothing.
A plain explanation of what the macro does, then a list of risks, each tied to the line it refers to.
Explain in plain language what this VBA macro does. Then list risks: hard-coded ranges, ActiveSheet use, missing error handling, silent overwrites. Quote the line each risk refers to. Do not rewrite the macro. Sheet and file names are generic. Macro: [paste macro]
View full prompt →Tip: Change sheet and file names to generic ones before you paste. Never include connection strings, file paths to shared drives or passwords. Macros written against company workbooks depend on your AI policy, so use an approved tool or keep a trimmed sample. Confirm each quoted line exists in the macro before acting on a risk.
A description of what each join and filter does, the places where rows could be dropped or duplicated, and a row-count check query to run before and after each join.
Explain what each join and filter in this query does. Say where rows could be dropped or duplicated. Write a row-count check query to run before and after each join. Table and column names are generic. No data is included. Query: [paste query]
View full prompt →Tip: Generalize table and column names and never paste real values, connection details or credentials. A query that names company tables depends on your AI policy, so use an approved tool or rename everything. Treat each claimed problem as a lead to confirm with a count, not a finding.
A list of questions a reviewer is likely to ask about your deliverable, grouped under data, method, calculation, documentation and communication.
My deliverable is [type]. My team's checklist already covers: [items]. List questions a reviewer is likely to ask, grouped by data, method, calculation, documentation and communication. Skip what my list covers. Make no claim about what any standard requires.
View full prompt →Tip: The prompt names a deliverable type only, so no company figures are needed. Ask the reviewing actuary or your model governance process what standards apply, because the AI cannot say. Look hardest at the data-trail questions, since those are the ones people skip.
A short email that states the result, what it means for the recipient and a clear ask, with every figure left as a placeholder.
Draft an email under 120 words to [recipient role]. Result: [three-line note with [figure] placeholders]. State the result, what it means for them and one clear ask. Plain language, no jargon. Add no figures and no conclusions beyond my note.
View full prompt →Tip: Keep real figures out of the prompt and add them to the draft afterward. Results are company data, so use an approved tool under your company's AI policy. If the draft says more than your note does, cut it back to the note.
A numbered list of reconciliation tests, each saying what to compare and giving an Excel formula or SQL pattern you can adapt, built only from your column names and the control totals your team gav...
Columns in my extract: [column names]. Control totals from my team: [totals]. Checks I already run: [checks]. List numbered reconciliation tests. For each, say what to compare and give an Excel formula or SQL pattern using my column names. Do not claim which test will explain the gap.
View full prompt →Tip: Type column names and invented or rounded totals only. The AI never sees the data, so every test runs on your side. Real in-force counts and control totals are company data, so check your company's AI policy and use an approved tool before typing them, or keep the invented sample and swap in the real figures in your own workbook. Delete the tests that do not apply to your extract before you file the list.
A short plain-language version of your technical note that says what was measured, what moved, what it does and does not mean, and which decision it feeds.
Rewrite my note for [audience] who needs to decide [decision]. Cover what was measured, what moved, what it does and does not mean, and the decision it feeds. Keep every figure as [figure]. Add no new numbers and no new conclusions. Note: [note]
View full prompt →Tip: Write the figures as [figure] in your note and fill them in from the workbook after the rewrite. Results can be confidential, so use an approved tool under your company's AI policy. If a sentence sounds more certain than the note does, soften it back.
Excel formulas for age-to-age ratios, a blank-safe version of them, and a check row that ties a diagonal total to a control figure.
My triangle: rows are accident years, columns are development ages, starting at [start cell]. Write Excel formulas for age-to-age ratios, a version that stays blank when a cell is empty or zero, and a check that ties the latest diagonal to [control figure]. Do not suggest factors, averages or a tail.
View full prompt →Tip: Paste the formulas into a copy and try an empty cell and a zero before you trust the blank-safe version. Choosing factors, the averaging method and any tail stays with you and the reviewing actuary. The prompt contains layout only, so no company data is needed.
A line-by-line explanation of a snippet, plus a tiny invented test case with the expected result worked out by hand so you can run it right away.
Explain this [R or Python] snippet line by line. Then give a tiny test case with invented rows and work out the expected result by hand, showing each step. The snippet was written for synthetic data. Snippet: [paste snippet]
View full prompt →Tip: Run the test yourself and compare with the hand result before you read anything into the explanation. Use synthetic data or column names only, never a real extract. A script that mentions company tables or paths depends on your AI policy, so use an approved tool or generalize the names.
A step-by-step reading of one formula in plain terms, followed by two or three inputs that could break it, so you know what to test before you rely on it.
Explain this Excel formula step by step: [formula with generic references]. Then list 2 or 3 edge cases that could break it, such as text in a numeric column, blank cells or a range that stops short. Plain language, one short paragraph per step.
View full prompt →Tip: Replace sheet names, product names and client names with generic ones before you paste, and paste the formula only, never the cells around it. The explanation can be wrong, so the scratch-copy test is the check. If a range stops short, check the last row of the real data in your own file.
A set of practice questions with worked answers on one topic, each with a note on the step people most often miss.
I am studying [topic]. Here are my own notes: [notes]. Write [number] practice questions with worked answers. For each, name the step most often missed. If you are unsure of an answer, say so instead of guessing.
View full prompt →Tip: AI answers can be wrong, so treat the material you are licensed to use as the authority. Confirm the syllabus, exam format and any exam policies on the SOA or CAS site, not from the chatbot. Never upload study manuals you are not licensed to share. Use your own notes or a topic name from the published syllabus.
A change log entry written the day of the change in your team's format, with the approver and the test reference left blank.
Write a change log entry from my notes. Changed: [what]. Why: [why]. Tested: [tests]. Use this format: [paste format]. Leave Approver and Test reference blank. Write [not stated] for anything I did not say. Add nothing new.
View full prompt →Tip: Write the notes in your own words and leave out values and client names. Describing changes to a company model depends on your AI policy, so use an approved tool. This prompt logs a change event. To document what a model step does, use the assumption log prompt instead.
A first draft of the observations section for an experience study, with a [figure] placeholder wherever a number belongs and a list of questions for your reviewer.
Below is an A/E table I already computed. Draft an observations section. Put [figure] wherever a number belongs. Do not explain why any cell differs and do not recommend an assumption change. Then list questions for the reviewing actuary. Table: [table]
View full prompt →Tip: Aggregated results can still be confidential company data, so use an approved tool under your company's AI policy, or paste an invented table with the same shape and fill in the real cells later. Read every sentence against the workbook. If the draft offers a reason for a difference anyway, delete it.
A consistent log entry for one model step, written in the format of a past entry, with a blank wherever you did not supply the detail.
Write a log entry in the format below. Step: [what it does]. Inputs: [inputs]. Approved by: [code], [date]. Use only what I wrote. Leave [blank] for anything missing. Never fill in an assumption value or a rationale. Format: [paste a past entry]
View full prompt →Tip: Describe the step without real values, and paste a past entry with its numbers removed. Model details can be company data, so check your AI policy and use an approved tool. The AI records what you wrote and nothing more. The assumption and its rationale come from the reviewing actuary.
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Recommended Tools
4Ranked by relevance for actuarial analyst
- 1
Claude
Data Reconciliation Checklist From Column Names and Control Totals, Draft an Actual-Versus-Expected Write-Up From Your Results + 2 more
BeginnerVerified Oct 2026 - 2
ChatGPT
Explain an Excel Formula in Plain Terms, Model Documentation and Assumption Log Entries + 2 more
BeginnerVerified Oct 2026 - 3
Microsoft Excel
Check an Exhibit Workbook With Copilot in Excel
IntermediateVerified Oct 2026 - 4
Zapier
Weekly Close and Valuation Task Digest
BeginnerVerified Oct 2026
Common questions
- What is the best AI tool for an actuarial analyst?
- 1. Claude: Data Reconciliation Checklist From Column Names and Control Totals, Draft an Actual-Versus-Expected Write-Up From Your Results + 2 more. 2. ChatGPT: Explain an Excel Formula in Plain Terms, Model Documentation and Assumption Log Entries + 2 more. 3. Microsoft Excel: Check an Exhibit Workbook With Copilot in Excel.
- How can an actuarial analyst use ChatGPT or another AI chatbot?
- Start with copy-paste prompts that work in any free chatbot. For example: A consistent log entry for one model step, written in the format of a past entry, with a blank wherever you did not supply the detail. A first draft of the observations section for an experience study, with a [figure] placeholder wherever a number belongs and a list of questions for your reviewer. A change log entry written the day of the change in your team's format, with the approver and the test reference left blank.
- Do I need technical skills to start?
- No. Level 1 prompts work in any free AI chatbot with no signup beyond the chatbot itself: copy the prompt, fill in the bracketed details, and paste it in. Later levels add AI features in tools you already use, then dedicated AI tools and automation.
New to AI?
The Big Four AI Platforms
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The AI Usage Ladder
See how you use AI today, where that sits on the map, and the one next step to try.
Aim Higher with AI
Doing the same work faster is the smallest win. Three ways to ask AI for more, and why most of us under-ask.
How to Keep Up with AI
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We update this guide when the tools change. See what's changed →