$277,800top of the range in New York · middle $130,000 / yr
AI is transforming this role
Actuarys in the United States earn a median of $130,000 a year. Pay starts near $78,570. Pay reaches $277,800 at the top of the range in New York, the best-paying state for this work among those with at least 500 people in the job.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Actuaries, SOC 15-2011). Last checked 9 September 2026.
Entry level
$78,570
Top of the range · New York
$277,800
Education
Bachelor's in actuarial science, math, or statistics
Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Actuaries). Top of the range is the highest state-level figure among states with at least 500 people in the job. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.
🆕 New & Trending AI Tools for ActuaryReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Actuary work right now.
NumericNEWPaid / see site
AI-driven month-end close, reconciliation, and reporting.
How an Actuary uses it: automate reconciliations and close the books faster
HebbiaNEWEnterprise / see site
AI that reads and analyzes large financial documents and filings.
How an Actuary uses it: pull answers out of contracts, filings, and reports in minutes
NotebookLMNEWFree / $7.99 mo
Google tool that answers questions grounded only in the documents you give it — with citations.
How an Actuary uses it: load your own manuals, policies, or PDFs and ask questions that stay accurate to the source
MindBridgeEnterprise / see site
AI that scans transactions for anomalies, errors, and fraud risk.
How an Actuary uses it: flag risky or unusual entries across the whole ledger, not just a sample
Vic.aiEnterprise / see site
Autonomous accounts-payable and invoice processing.
How an Actuary uses it: let AI code and process invoices with minimal manual entry
RampFree core / paid
Finance platform with AI that automates expenses and spend controls.
How an Actuary uses it: auto-categorize spend and catch policy issues in real time
Power BI Copilot$10+ mo
Microsoft analytics with AI that builds dashboards and explains trends.
How an Actuary uses it: ask questions of financial data and get charts and forecasts back
ChatGPTFree / $20 mo
The most-used AI assistant — writing, analysis, research, and images from a plain-language chat.
How an Actuary uses it: draft emails and documents, summarize long files, and get instant answers to on-the-job questions
ClaudeFree / $20 mo
AI assistant known for careful writing, long-document analysis, and coding.
How an Actuary uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing
The opinion letter is on the screen and the reserve committee meets in an hour. A credentialed actuary reads the conclusion twice: the amount the company should hold, the methods behind it, and the changes since last quarter that a director will ask about. An analyst built the triangles and the first draft. Peer review already marked up the workbook. What remains is the signature, which carries the whole letter. It tells the board, the auditor, and the regulator who looks at the filing that a qualified actuary stands behind the number. Down the hall, a pricing team wants a decision on a rate filing before the market window closes. Both waits are aimed at the same kind of person: someone whose credential matches the opinion.
Someone seeking this seat, rather than the analyst desk that feeds it, should already be able to own a result in public. The work is pricing, reserving, or enterprise risk. The audience is leadership that will spend money, hold capital, or change a product because of what the actuary writes.
Analyst, associate, fellow, then the chair that signs for the company
The route runs in a line that is hard to skip. It starts as an analyst who assembles data and a first draft. It moves through associateship, the first professional level granted by the Society of Actuaries or the Casualty Actuarial Society, depending on the industry. It continues to fellowship, the second professional level from the same body, which is the credential most opinion seats expect. Only after that do titles such as chief actuary or consulting partner become realistic. Chief actuary means the person accountable for the actuarial view inside an insurer or a similar institution. Consulting partner means the person who wins the engagement and signs the advice a client will book or file.
Each step changes the kind of mistake that matters. An analyst's mistake is a bad tie or a draft that cannot be rerun. An associate's mistake is a thin review of someone else's draft, or a client note that sounds finished when it still needs a fellow. A fellow's mistake is an opinion that was convenient and later proved indefensible. A chief actuary's mistake is a culture in which bad news arrives too late because the team learned to soften it. People who want the last chair should practice the earlier ones in order. Firms rarely hand a signature to a charming generalist who skipped the sequence.
Lateral moves happen, and they still respect that order. A fellow in a consulting firm may become a chief actuary at a company whose book the fellow already understands. A company actuary may become a consulting partner after years of being the client who knew which advice was solid. What travels is the credential, the line of business, and a record of opinions that did not have to be walked back. What does not travel is a title alone. A "head of actuarial" hired without fellowship, into a seat that must sign, will spend the first year discovering the limit.
Pricing, reserving, and risk across the whole enterprise
Pricing, for a credentialed actuary, is the decision about what to charge and which risks to accept, not the first pass at a spreadsheet. The actuary reviews the indicated need, the competitive position, and the segments where the current rate is hiding a loss. The memo says what to file or what to quote, where to hold the line, and which assumption would flip the recommendation. Underwriters and product managers are the daily partners. They bring market pressure. The actuary brings a cost of risk that does not move just because a competitor posted a lower price.
Reserving is the decision about obligations already in the door. Reported claims, claims not yet reported, and the expenses of handling them all belong in the view. The actuary chooses methods, challenges the ones that flattered last year, and explains movement in a way finance can book and an auditor can test. A reserve opinion is a formal statement. When the filing requires a name, only that statement will do. The actuary who signs has to have done enough personal review to mean it, which includes looking at large claims, changing mix, and any place the data stopped matching the story the business tells.
Enterprise risk work pulls the lens wider than one product. The actuary helps leadership see which risks, taken together, could threaten the company's ability to pay what it owes. That might be a clash of investments and liabilities, a catastrophe concentration, a medical-cost surge, or a pension promise growing faster than the assets set aside. The advice sounds like a choice: hold this risk, reinsure it, hedge it, or stop writing it. The advice fails when it is a catalog of worries with no priority. Directors need to know what changed since the last discussion and what decision is actually in front of them.
The week has a private rhythm underneath the meetings. Early, the actuary reads exceptions the team flagged: a loss ratio that jumped, a census record that failed a provision, a capital measure that moved because asset values moved. Midweek is for review notes written so the analyst can act without a second meeting. Late in the cycle the actuary rereads only the pages a stranger will see, because those pages are what the signature covers. Anything the actuary cannot explain from those pages goes back to the file before it goes to the board.
In all three settings the actuary manages people who are still on the exam sequence. Reviewing their drafts, teaching them why a check exists, and refusing to sign work the reviewer did not understand are the daily management tasks. Meetings fill a calendar. The work that counts is a number that survives contact with an auditor, a regulator, a board, or a client who dislikes it.
Who may hold out the credential
The Society of Actuaries grants associateship and fellowship for life, health, retirement, and related paths. The Casualty Actuarial Society grants associateship and fellowship for property and casualty paths. Those two levels are the professional structure. People reach them through validated coursework and a long sequence of exams the society administers. Employers hire and promote around that structure. They do not invent a private version of it. A candidate describing preparation should say which society, which level is complete, and that the work of preparation was coursework the society validated plus the exam sequence, taken while employed.
In the United States, many credentialed actuaries also join the American Academy of Actuaries, the national professional association, whose site is actuary.org. Membership there is part of how the profession organizes its public voice and its qualification to sign certain statements. It does not replace associateship or fellowship from the society that fits the work. A resume should list the society designations exactly, the year they were granted, and the line of business in which the person has actually signed or reviewed. Vague phrases such as "actuarial certified" help no one and invite a check that will embarrass the candidate.
Qualification to sign a particular statement depends on the credential and on experience in that kind of work. A fellow in pensions brings the wrong experience to a catastrophe reserve, and a casualty fellow brings the wrong experience to a retiree medical valuation, even though both people are actuaries. Hiring managers and boards look for the match. The person who wants the seat should be precise about the opinions already signed and the ones still reviewed by someone else.
How employers choose a signer
Recruiting at this level is a search, not a campus fair. Insurers, consulting firms, and large pension sponsors look for a fellow with a book of work they can recognize. The interviews go deep on one or two past opinions: what the data did, what the actuary rejected, what the board or the client pushed back on, and what the actuary would do differently. References are often other actuaries, because the community is small enough that a careless opinion is remembered.
Candidates should bring a narrative of responsibility that matches the posting. A pricing lead role wants filings, indications, and the arguments with underwriting. A reserving role wants opinions, auditor reviews, and a story about a year the number had to move in an unpopular direction. An enterprise risk role wants a record of talking to a board about capital and about what the company should stop doing. Pretending to have signed work that was only supported from the side is a known failure mode. The interviewer will ask who else was on the letter.
Searchers also listen for how the actuary treats pressure. A useful story is a quarter when new claims arrived ugly and the indicated reserve rose, and the actuary explained the rise before anyone asked for a smaller number. Another is a product the actuary recommended leaving, with the later result that justified the exit. Stories that feature only victories, or only obedience to sales, worry a board that has already lived through a surprise. The candidate who can describe a disagreement with a chief financial officer, including how the disagreement was documented, sounds like a signer.
The practical package around the credential matters too. Can the person review a model without rebuilding it from scratch every time, and still know when a rebuild is required? Can the person hire analysts and keep them progressing? Can the person write a page a director will finish? Those are the skills that turn fellowship into a chief actuary or a consulting partner. Technical brilliance that cannot be communicated stays in a specialist corner, which is a respectable career and a different posting.
A negotiation that respects the occupation's published range
This occupation's pay on the page is drawn from Actuaries under SOC 15-2011 in the May 2025 Occupational Employment and Wage Statistics tables. Use those figures as the shared reference in an offer conversation, and match the figure to the chair.
Someone still crossing from analyst toward a first opinion is nearer the entry level of $78,570 than to the middle of the profession. The median of $130,000 is the better anchor once the person holds fellowship and regularly owns pricing, reserving, or enterprise-risk conclusions. The published step between those two points is $51,430. A fellow whose offer still sits near entry can name that step and ask which part of the signing responsibility the employer thinks is absent. A new analyst quoting the median back at a chief actuary search is having a different conversation than the one on the table, and should not borrow this paragraph's logic.
New York is where the high end of the published range reaches $277,800. That figure is the top of the range in a place with enough actuaries for the Bureau to publish it. It is distinct from typical pay in New York, which is a median of $156,480. Connecticut's typical pay is $166,800, a state median $36,800 above the national median and the highest median among the states listed here. Typical pay runs $142,800 in New Jersey, $132,110 in Florida, and $131,640 in Wisconsin. A chief actuary or a consulting partner weighing a New York role can discuss $156,480 as typical local pay and can mention $277,800 only as the high end of the published range, appropriate when the package reflects origination, a signature, and a market the Bureau measured at that height.
The distance from the national median up to the New York high end is $147,800. That distance belongs in a benchmarking talk for the top of the profession. It is too wide to treat as a bargaining chip for a single promotion from associate to fellow. Bring one or two figures, name the series, keep state medians separate from the high end of the range, and tie the ask to work the person has already signed. A signer who cannot explain a pay request with that kind of restraint will have trouble explaining a reserve movement the same way.
The top of Actuary pay — and how to get there with AI
$277,800what Actuary pay reaches in New York
Highest state-level top-of-range annual wage for Actuaries, among states with at least 500 people in the job. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.
And the role it leads to — Financial Managers — reaches $370,780 in New York.
$78,570entry$130,000middle$277,800top end
An actuary in the middle of this range works inside whatever platform was bought years ago; one at the top chose it, priced the switch, and can defend the choice under questioning.
Somebody selected the reserving package, the compliance testing software and the database behind them, and that somebody is usually not an actuary, which is why the tools fit the work badly. The parts of this job that get expensive when tooling is wrong are exactly the visible ones: determining policy contract provisions for each type of insurance, working out an equitable basis for distributing surplus earnings, and sitting with programmers, underwriters and claims experts on a new line of business. A model can compress the dull half of an evaluation, reading vendor documentation and drafting test scripts, but only somebody who does reserving knows which questions a demo is dodging. That combination is scarce and it gets paid.
Your playbook, by where you are now
Just startingCost out the stack you already use
Log a fortnight of your own reserving work in fifteen-minute blocks and mark every wait, re-run and hand-keyed factor.
Rebuild the slowest extract as a Microsoft SQL Server query and record what the run time became.
Have Claude condense the release notes for the packages you already run, then check each claim against the software before you repeat it to anyone.
Ask to listen in on one licence renewal call and write down which questions the vendor answered thinly.
What proves it: A timed before-and-after of one recurring calculation, with the raw log attached.
Realistic span: the first year or two
A few years inRun an evaluation people can audit
Write the requirement list for one tool your team depends on: what it must output, what it must tie to, what a regulator will ask it to reproduce.
Score the incumbent, whether that is ARMON Technologies XLActuary or an inherited Microsoft Access build, against two alternatives in a workbook anyone can open.
Put the migration into Microsoft Project so the switching cost is a dated schedule instead of a guess.
Draw the data flow in Microsoft Visio with each hand-off marked, so a programmer and an underwriter each see their own piece.
Test one candidate on a closed period and publish where its numbers part company with yours.
What proves it: An evaluation memo with scoring behind it that a purchase decision actually followed.
Realistic span: roughly years three to six
ExperiencedHold the standard and the contract
Set what compliance testing software must demonstrate before any filing leaves the department.
Put the tooling case to executives in their terms: contract provisions priced sooner, fewer restatements, a reinsurance negotiation prepared in days rather than weeks.
Reopen one vendor contract using the usage evidence you have been collecting all along.
Bring the surplus distribution and reinsurance work onto the platform yourself so nobody can call the choice theoretical.
What proves it: A production platform selected on your memo, renewed on your numbers.
Realistic span: year seven onward
The next 90 days
Choose the tool your team grumbles about most and spend ninety days building the case nobody has built. Write one page: what the tool has to produce, what has to reconcile to the general ledger, what a public agency would demand it reproduce on request, and what the current version costs in staff hours during a close. Get the hours from a real log, not memory. Then ask two vendors the same five questions and write down who answered and who deflected. That page is a small piece of work, but it puts an actuary in the middle of a spending decision, and people who sit in spending decisions get asked into the next one.
Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.
Every figure is the national median from the U.S. Bureau of Labor Statistics (OEWS) shown on that role’s own page.
Never used AI before? Start here (2 minutes).
Move your work from spreadsheets to code. The highest-leverage step for a modern actuary is fluency in Python or R — open one and rebuild a real reserving triangle (the chainladder package) or a pricing GLM you currently run in Excel. Reproducible, auditable code is faster, less error-prone, and the foundation of every advanced technique.
Use ChatGPT or Claude to explain methods, scaffold and debug code, and draft documentation — with generic or synthetic data only. Keep all policyholder and company data inside approved, governed systems. Learn the code first; the ML follows.
The one rule, forever: Actuarial work is regulated and consequential. Every model must be validated, documented, and explainable — and must not encode unfair discrimination or use proxies for protected classes, which regulators (and laws like Colorado's SB21-169) increasingly police. Never paste confidential policyholder data or PII into a public AI tool; use approved, governed environments. AI accelerates the modeling, but the credentialed actuary owns the assumptions, the validation, and the signed opinion under the ASOPs.
The plays — exact steps, exact prompts
Do these in order. Each one is copy-paste ready. You do not need to know anything about AI going in.
1
Upgrade pricing from GLMs to explainable machine learning
Why this pays: Pricing sophistication is the profit engine of an insurer. An actuary who builds ML-enhanced pricing models — more predictive, still explainable and compliant — directly improves loss ratios and gets pulled onto the pricing and analytics teams that pay the most.
Akur8Python (scikit-learn, XGBoost)R (tidymodels)
1
Prototype a gradient-boosted or ML-enhanced pricing model in Python/R, or use Akur8 (built for transparent, regulator-ready insurance pricing) to blend GLM interpretability with ML lift.
2
Use AI to design a compliant modeling workflow.
Copy-paste this prompt
Act as a pricing actuary. Outline a workflow to move a [personal auto] GLM to a gradient-boosted model while keeping it explainable and compliant: feature engineering, guarding against proxies for protected classes, training/validation, using SHAP for interpretability, comparing lift vs. the GLM, and documenting it for regulatory filing. Note the fairness and ASOP considerations at each step. Use generic, non-confidential framing.
Test explicitly for disparate impact and proxy discrimination; a more predictive model that isn't fair or explainable is a regulatory and ethical failure.
3
Validate lift, monitor stability, and write the model documentation a regulator would accept — you own the filing.
What you'll haveMore predictive, defensible pricing that improves loss ratios — the analytics impact that leads to pricing-lead and chief-actuary pay.
2
Automate reserving and valuation in reproducible code
Why this pays: Reserving and valuation cycles eat weeks of manual spreadsheet work. An actuary who automates them in code runs more scenarios, closes faster, and catches errors early — the reliability and speed that senior reserving and valuation roles reward.
Python (chainladder)Prophet (FIS)R
1
Rebuild your reserving triangles and diagnostics in Python's chainladder package (or automate runs in Prophet/AXIS) so a full re-reserve is a script, not a week of copy-paste.
2
Have AI scaffold and stress-test the reserving code.
Copy-paste this prompt
Write Python using the chainladder package to load a paid-loss triangle, apply chain-ladder and Bornhuetter-Ferguson, produce IBNR and reserve estimates with Mack standard errors, and generate diagnostic plots and a bootstrap distribution for reserve variability. Explain each method's assumptions and when it breaks down so I can pick the right one. Use synthetic data.
Automation speeds the mechanics; you still select methods, judge tail behavior, and own the reserve opinion under the ASOPs.
3
Add automated checks (reconciliations, prior-period diagnostics) so every close is faster and cleaner.
What you'll haveFaster, auditable reserving with more scenarios and fewer errors — the dependable rigor behind senior valuation roles.
3
Compress the exam path with an AI study engine
Why this pays: Every SOA/CAS exam and credential (ASA, FSA, ACAS, FCAS) is a direct raise and a gate to senior roles. An actuary who uses AI to learn faster and pass sooner reaches fellowship — and the pay that comes with it — years ahead of peers.
ChatGPTClaudePython
1
Use ChatGPT or Claude to explain tough syllabus concepts in plain language, generate practice problems, and critique your worked solutions.
2
Build a personalized exam plan and drill your weak spots.
Copy-paste this prompt
Act as an actuarial exam tutor for [SOA Exam FM / CAS Exam MAS-I]. Build a [12-week] study plan from my start point [describe], generate a set of practice problems on [the topics I'm weakest in], and after I attempt each, critique my solution and show the efficient method. Create spaced-repetition flashcards for the formulas I keep missing.
Verify AI explanations against the official syllabus and study manuals; use it to accelerate understanding, not to replace working problems.
3
Code the models you're learning (a life table, a GLM) so exam concepts become working skills.
What you'll haveCredentials earned faster — each exam and fellowship a direct step up the actuarial pay ladder.
4
Automate model documentation, reporting, and data wrangling
Why this pays: Actuaries lose huge time to data cleaning, documentation, and reports. Automating that work frees you for the analysis and judgment that differentiate you — and clear communication of results is exactly what gets actuaries promoted into leadership.
Python (pandas)ClaudePower BI
1
Script data cleaning and validation in Python, and use Claude to draft first-pass model documentation and technical reports from your notes.
2
Turn results into stakeholder-ready communication.
Copy-paste this prompt
Draft an executive summary of a [pricing/reserving] analysis for a non-technical audience (underwriting leaders, the board): the key findings, what changed and why, the assumptions and their sensitivity, the risks, and a clear recommendation. Then give me a one-page technical appendix outline for the actuarial reviewers. I'll insert the actual figures; keep it generic.
Keep confidential figures out of general AI; verify every number and characterization before it's shared.
3
Build reusable dashboards in Power BI so results update automatically for stakeholders.
What you'll haveTime reclaimed for judgment and clearer results for decision-makers — the communication edge that lifts actuaries into leadership.
5
Move upstream into predictive analytics and emerging risk
Why this pays: The best-paid modern actuaries work where demand is exploding — predictive analytics, catastrophe and climate risk, health and behavioral models. Building that data-science depth positions you for the analytics-leadership and consulting roles at the very top of the band.
Python (PyTorch)RDatabricks
1
Pick a high-demand domain (climate/cat risk, health analytics, customer/lapse modeling) and build a real project on public data using Python/R.
2
Use AI to build the upskilling path.
Copy-paste this prompt
Act as a mentor for an actuary moving into predictive analytics. Build a 6-month plan to add modern data science to my actuarial base: topics (feature engineering, tree ensembles, model validation, ML fairness, intro to neural nets), one hands-on insurance project per month using public datasets, and how to frame the work for pricing/reserving leadership. List the SOA/CAS resources on predictive analytics I should use.
Ground every technique in an actuarial problem and the professional standards; ML is a tool serving the actuarial opinion, not a replacement for it.
3
Publish an internal case study of a model and the value it created — visibility drives the move into leadership.
What you'll haveData-science depth on top of actuarial rigor — the profile that commands the top analytics and consulting roles.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $277,800 tier.
Month 1
Rebuild one real reserving triangle or pricing GLM in Python/R. Get comfortable with reproducible, auditable code.
Months 2-3
Prototype an explainable ML pricing model and validate it for lift and fairness against your GLM.
Months 3-6
Automate a reserving/valuation run and your model documentation and reporting.
Months 6-12
Go deep in a predictive-analytics domain and publish an internal case study — the path to analytics leadership.
Gear for this job
As an Amazon Associate, PayCrunch earns from qualifying purchases. Links to books and tools are for the job on this page; we only recommend what we’d use in the work.
Pearson for live SOA Exam P (May 2026: 30 items / 3 hours; univariate 44–50%). This is P, not FM.
Next steps for an Actuary
Some links below are affiliate or partner links. PayCrunch may earn a commission if you enroll or subscribe through them, at no extra cost to you. Wage figures on this page still come from the Bureau of Labor Statistics, not from these programs.
Actuary work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Actuaries (SOC 15-2011). O*NET Job Zone 4 is typical: a bachelor's degree, so the honest next credential is a professional certificate or bachelor's-level coursework — not a random catalog dump.
The occupation's listed knowledge areas include Economics and Accounting and Law and Government; the links search those subjects, not a generic 'career courses' list.
Actuaries in this dataset list C++ among the tools in use, so a program that names that stack is a better fit than a survey course.
Coursera search for actuarial science — a professional certificate or bachelor's-level coursework that lines up with computing, not a generic professional-development aisle.
FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Actuary work, not a claim that they list a counted SOC 15-2011 inventory.
Write an Actuary resume, or one aimed at Financial Managers, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.
An Actuary resume that names the actual tasks on this page, or the step-up title Financial Managers, beats a blank template when you apply.
What Actuaries earn by state
These are the Bureau of Labor Statistics’ own figures for Actuaries, state by state — not a cost-of-living adjustment applied to the national number. Only states employing at least 500 people in the occupation are shown, because a state median drawn from a handful of workers is noise rather than a signal.
Connecticut
$166,800
highest of them · +28% vs the national median
Michigan
$100,640
lowest of the 17 states that qualify · -23% vs the national median
The same job pays $66,160 more a year at the median in Connecticut than in Michigan — 66% higher. That gap is what the Bureau measured, before any question of what it costs to live in either place. The top-of-range figure quoted at the head of this page, $277,800, is a different statistic in a different place: it is the 90th-percentile wage in New York. The state that pays the typical worker most and the state where the best-paid go highest are not always the same one.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, SOC 15-2011. 17 states clear the 500-employee reporting floor for this occupation; those below it are left out rather than shown with a wide error band.
Free data. Use any of it.
PayCrunch publishes verified, BLS-sourced salary + AI-playbook data on 1,000+ professions — free, no signup.
No, but it is reshaping the job. AI can't choose assumptions, defend a model to regulators, or sign an actuarial opinion under professional standards. It automates the modeling and documentation around those judgments. Actuaries who master ML and code are becoming more valuable; those who stay spreadsheet-bound are the ones at risk.
What's the highest-leverage skill for a modern actuary?
Programming — Python or R — with a real grasp of machine learning. Nearly every advanced pricing, reserving, and analytics workflow runs on code now, and fluency in it is what separates actuaries heading into leadership from those stuck in manual spreadsheets.
Is it safe to use ML in insurance pricing?
Only if it's explainable and fair. Regulators require models you can justify, and laws increasingly prohibit proxies for protected classes. A black-box model that lifts loss ratios but can't be explained, or that produces disparate impact, is a compliance and ethics failure — you must validate, document, and own it.
Can I put company data into ChatGPT?
No. Policyholder data and PII must never go into consumer AI. Use general tools with synthetic or generic data for learning, code, and drafting, and keep all real data inside approved, governed systems. The actuary is accountable for confidentiality and for the model.
How does AI actually raise an actuary's pay?
Two ways: credentials and capability. AI compresses the exam path to fellowship, each exam a raise, and it lets you build the ML-driven pricing and analytics that improve results — the work that leads to pricing-lead, chief-actuary, and consulting-principal roles at the top of the band.
Methodology & sources
Salary (median, 10th, top of the range) — U.S. Bureau of Labor Statistics, OEWS.
By state — the Bureau of Labor Statistics’ own state medians, limited to states employing at least 500 people in the occupation. No cost-of-living arithmetic is applied to a wage anywhere on this page.
The plays — PayCrunch's own step-by-step guidance using publicly available AI tools. Tool names/URLs are real and current as of August 2026; prompts written to work as-is. Verify any professional output before relying on it.