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PayCrunch AI Playbook · Finance

The Actuarial Consultant who carries the relationship

$277,800top of the range in New York · middle $130,000 / yr
AI is transforming this role

Actuarial Consultants 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 degree in Math or Actuarial Science
Lower disruption Higher exposure AI is transforming this role
Entry · $78,570 Top of range · $277,800 (New York) Middle $130,000

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 Actuarial ConsultantReviewed September 2026

We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Actuarial Consultant work right now.

NumericNEWPaid / see site

AI-driven month-end close, reconciliation, and reporting.

How an Actuarial Consultant uses it: automate reconciliations and close the books faster

HebbiaNEWEnterprise / see site

AI that reads and analyzes large financial documents and filings.

How an Actuarial Consultant 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 Actuarial Consultant 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 Actuarial Consultant 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 Actuarial Consultant 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 Actuarial Consultant 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 Actuarial Consultant 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 Actuarial Consultant 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 Actuarial Consultant uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing

A product lead at an insurer has two prices on a slide and a board meeting on Thursday. The actuarial consultant on the phone did not invent either price in the meeting. The work happened earlier: a data pull of the client's own claims, a model that turns those claims into a future cost, and a memo that says what changes if the new benefit is richer than the old one. The lead does not need a lecture on probability. The lead needs to know which price keeps the product viable, which price will lose the business, and which assumption, if wrong, would make both prices misleading. The consultant's job is to put that choice in writing a non-actuary can defend.

Someone who wants this seat should picture client work, not a private research post. The week mixes models, peer review inside the firm, and conversations with underwriters, finance chiefs, and pension trustees who will act on the number. The deliverable is a recommendation with the reasoning beside it.

Pricing, reserves, and pensions in language a client can use

Pricing work answers a commercial question without hiding inside jargon. An insurer wants to know what to charge for a policy so that claims, expenses, and a margin for uncertainty can be met. The consultant starts from the client's history when that history is deep enough to trust, and from broader industry patterns when the product is new or the block of business is small. The output is not a single mysterious figure. It is a price, a short list of assumptions that move the price, and a plain statement of what the client would be betting if those assumptions slip. A product manager should be able to choose, and to explain the choice upstairs, after reading the memo once.

Reserve work answers a different fear: whether money already set aside will cover obligations the company has already taken on. Some claims have been reported and are still open. Some events have happened and have not been reported yet. Some policies will produce costs later even though nothing looks wrong today. The consultant tests the current reserve against the client's data and against methods a peer can retrace. The memo to a finance chief should say whether the reserve looks thin, adequate, or heavy, and what would change the view next quarter. A board does not need the spreadsheet. It needs the direction and the reason.

Pension work is about a promise to pay people later. The client may be a company, a public plan, or a board of trustees. The consultant estimates the value of benefits already earned, the contribution that fits the plan's funding rules and the sponsor's situation, and the way the promise shifts if people live longer, retire earlier, or receive pay increases the plan must recognize. The written advice has to be usable by a treasurer who will write a check and by a trustee who will be asked, in a meeting, why this year's contribution moved. Charts help only when the sentence under the chart says what to do.

The tools are ordinary and the judgment is not. Consultants live in spreadsheets a second person can audit, in queries that pull claims or census records without silent filters, and in versioned files so last quarter's assumption can be compared with this quarter's. A beautiful model that nobody else can rerun is a liability on a client engagement. So is a model that copies last year's factors and never checks whether the client's mix of business changed. Before a number leaves the building, a reviewer who did not build it should be able to see the source data, the adjustment, and the sentence that will appear in the client letter.

Clients also ask what the consultant refuses to pretend. A thin data set, a benefit the carrier has never paid, or a pension population too small to support a fine estimate should be labeled as such in the memo. The useful sentence offers a range of outcomes tied to named assumptions and tells the client which assumption dominates. That habit is what separates advisory work from a calculation dropped into a slide. It is also what a partner listens for when deciding who may speak in the meeting without a script.

Across all three, the daily craft looks similar. Request the data and notice what is missing. Build or update the model. Compare this run with the last one and explain every movement a skeptical partner will spot. Write the client note in short paragraphs. Sit in the meeting and answer the follow-up without retreating into notation. Then archive the file so another consultant can pick it up if the engagement continues next year. Firms also expect the consultant to help sell the next phase of work, which means listening for the decision the client still has not made.

Associateship, fellowship, and the body that grants them

Consulting actuaries in the United States typically follow one of two credentialing bodies. The Society of Actuaries credentials people who work in life insurance, health insurance, retirement, and related financial risk. The Casualty Actuarial Society credentials people who work in property and casualty insurance: auto, home, liability, and similar covers. A candidate picks the society that matches the industry the job will sit in. Switching later is possible and costly in time, so the first employer is also a choice of path.

Each society recognizes two professional levels. Associateship is the first. It shows the person has completed the validated coursework the society requires and has come through the earlier portion of that society's exam sequence. Fellowship is the second level. It shows a deeper specialty and the rest of that long sequence. The society, not an employer and not a state licensing board, grants the designation. Employers treat fellowship as the mark of a fully credentialed actuary and treat associateship as a serious professional milestone along the way. A consultant may hold either, depending on the seat. Client-facing opinion work often sits with a fellow, while parts of the model may be built by people still moving through the sequence.

Preparation is validated coursework plus a long sequence of exams, usually sat while the person is already employed. Study happens around client deadlines. Firms that hire consultants generally expect steady progress and will say so in the offer. The Society of Actuaries publishes the pathway for its own designations. The Casualty Actuarial Society does the same for its track. A candidate should read the pathway of the society the target job uses, then describe progress in interviews as coursework completed and exams completed, without turning the conversation into a recital of logistics.

How a consulting firm decides to hire

Firms hire from university programs that feed the profession, from insurer rotations, and from other consultancies. The resume that works names the society, the progress already made, the tools used on real data, and one project a non-specialist could understand. "Built a model" is weak. "Priced a small-group health renewal and wrote the note the underwriter used" or "tied a pension valuation to the census the sponsor provided" tells a partner what the person can be put in front of a client to do.

Interviews test both the math and the translation. A case may hand the candidate a simple block of claims and ask for the drivers of a rate change, then ask for the three sentences the candidate would say to a chief financial officer. People who can only compute, and people who can only smooth-talk, both struggle. The firm is buying a colleague who will not embarrass the engagement. Communication under time pressure matters as much as a clean spreadsheet. So does honesty about a figure the candidate would not yet sign.

A writing sample, even a redacted one from a class project or an internship, should show the shape of advice: the decision, the evidence, the assumption that matters most, and what the consultant would revisit next time. Partners skim for jargon that hides a weak conclusion. They also notice whether the candidate can say "I would not sign this yet" and explain what is missing. That sentence is safer than a bluff.

Internships convert often in this field because the firm has already watched the person handle a deadline and a messy extract. A candidate without an internship can still enter through an insurer analyst program and move to consulting after a few valuation cycles, once there is a track record and exam progress to show. Either route should include SQL or another way to shape data, a spreadsheet a reviewer can audit, and writing samples that sound like advice rather than like a textbook chapter.

From a client team to a partnership

Inside a consultancy the titles vary, and the substance moves from doing the analysis, to owning the client relationship, to bringing in work and signing the opinion. Early consultants run pieces of a pricing, reserve, or pension engagement under a credentialed reviewer. Mid-level consultants design the approach, manage the data request, and draft the advice. Senior people sit with the client's leadership and carry the firm's name on the letter. Partnership adds sales, staffing, and the risk of the recommendation.

Some consultants leave for an in-house role: pricing lead at an insurer, chief actuary of a smaller company, or the pension actuary a sponsor wants on staff. That move trades variety of clients for depth in one book of business. It can be the right trade once the person knows which industry they want to stay inside. What pushes a person upward in either setting is the same pair of facts: credentials that match the signature the seat requires, and a record of advice that clients followed without a surprise the following year.

Putting the actuary wage series next to a consulting offer

The figures on this page are the Actuaries series, SOC 15-2011, from the Bureau of Labor Statistics Occupational Employment and Wage Statistics release for May 2025. They describe actuaries as an occupation. A consulting offer should be compared with that occupation, with eyes open about where a given seat sits inside it.

Entry pay on the series is $78,570. The median is $130,000. The rise from entry to median is $51,430. A consultant early in the exam sequence, still working under heavy review, has a coherent reason to treat $78,570 as the floor reference and to ask how quickly the firm moves pay as associateship comes into view. A consultant who already owns client memos and holds a fellowship can point at $130,000 as the occupation's middle and ask where the offer sits relative to that middle, given billable responsibility and a signing role. The $51,430 gap is the published distance between those two anchors. It describes the chart. It does not obligate a firm to cross it on a single promotion.

The high end of the published range in New York is $277,800, among places with enough actuaries for the Bureau to release a figure. New York's median, the typical wage in that state, is $156,480. Those are different facts. Typical pay in New York is $156,480. The high end of the range there is $277,800. Connecticut's median is higher than New York's median: $166,800, which is $36,800 above the national median. New Jersey's median is $142,800, Florida's is $132,110, and Wisconsin's is $131,640. A consultant choosing among offices can set an offer beside the state median and keep the New York high end out of the sentence unless the role truly resembles the top of the published range: a senior client owner in a market the Bureau measured at that height.

From the national median to that New York high end is $147,800. The span is useful when a principal or a partner-track consultant is benchmarking a package that includes origination and opinion risk. It is a clumsy number to wave at a first consulting offer. Name the anchor that matches the seat, name the Actuaries series, separate state medians from the high end of the range, and stop. The client-facing skill the firm is hiring is the same skill that makes this conversation short and exact.

The top of Actuarial Consultant pay — and how to get there with AI

$277,800what Actuarial Consultant 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

Mid-range consultants produce the analysis; the ones paid at the top of the range sit in the room where the client decides what to do about it, and get invited back.

Look at the tasks that sit at the client end of this job: explaining complex technical matters to executives, government officials, shareholders, and policyholders, negotiating reinsurance terms, testifying before public agencies on proposed legislation, and advising on a contract basis. None of those are model work, and all of them are what clients actually buy. Meanwhile the modelling itself keeps getting cheaper, so an actuary whose entire value is producing the number is competing against a falling price. Assistants can now compress the preparation, reading a filing record or drafting a first explanation, which makes it easier than ever to spend your hours on the conversation instead.

Your playbook, by where you are now

Just startingLearn to be understood

  1. Write one pricing result three ways: for the underwriter, for a chief financial officer, and for a regulator reading it cold.
  2. Reduce that result to a single Microsoft PowerPoint page with the assumption that drives it named in ordinary words.
  3. Have Claude attack your explanation the way a skeptical executive would, then close the two gaps you could not answer.
  4. Join client calls as the note-taker and send the follow-up summary the same day, every time.

What proves it: A one-page summary a non-actuary repeated back to you correctly.

Realistic span: your first eighteen months

A few years inOwn a piece of the engagement

  1. Prepare one reinsurance negotiation end to end: exposure data, loss history, and the terms you will not move on.
  2. Build the client a Microsoft Power BI view so they can watch their risk change between your visits.
  3. Draw the decision flow behind policy contract provisions in Microsoft Visio, so the client can see where each choice bites.
  4. Load prior orders and filing correspondence into NotebookLM before a rate hearing so you answer from the record rather than memory.
  5. Volunteer for testimony support work; building exhibits for a filing teaches you fast what survives questioning.

What proves it: A renewal or filing where the client's decision followed your recommendation.

Realistic span: years three through six

ExperiencedBring the work in

  1. Take the scoping call yourself and write the proposal, including a plain statement of what you will not do.
  2. Testify, or second-chair someone who does, on legislation affecting the lines you price.
  3. Construct a probability table for an exposure your firm has no product for yet and take it to executives with a business case attached.
  4. Bring a programmer and an underwriter into the first meeting rather than the fifth.
  5. Circulate an internal view on the risk your clients keep raising, and let it travel.

What proves it: Renewals and referrals that name you personally.

Realistic span: seven years and beyond

The next 90 days

Find the next result you have to present and rebuild the explanation from scratch for a listener with no mathematics behind them. One page. One sentence naming the assumption everything hangs on, one sentence on what happens if that assumption is wrong, and one sentence on what you recommend. Practise it aloud until it survives interruption. Then ask the senior consultant on the account whether you can deliver that part yourself. Doing this a dozen times turns a technical actuary into someone a client asks for by name, and being asked for by name is what carries pay toward the top of this range, especially in markets like New York.

Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.

Careers related to Actuarial Consultant

Similar pay, same field

Where this can lead

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).

Open Microsoft 365 Copilot in Excel. The spreadsheet is still where most actuarial work lives, and Copilot can build formulas, pivot experience data, and explain a workbook you inherited in seconds. Pair it with Python (the chainladder and lifelib libraries) driven by GitHub Copilot to move reserving and projection work from hand-built spreadsheets to reproducible code you can defend.

For learning, research, and drafting, use Claude or ChatGPT for methods and client memos, Perplexity for regulation and market data with citations, and NotebookLM to turn ASOPs, exam syllabi, and rate filings into a queryable study set. Keep all client and policyholder data inside approved systems; use general tools on general questions only.

The one rule, forever: Actuarial work is governed by the Actuarial Standards of Practice - you own every assumption and result, and AI is a tool, not a signer. Never paste identifiable policyholder, claimant, or confidential client data into a consumer AI tool; validate every AI-built model and reconcile its output before it goes in a report. Model risk under ASOP 56 is your responsibility, not the vendor's.
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
Move reserving and projections into reproducible, AI-assisted code
Why this pays: Consulting realization depends on how fast you turn data into a defensible answer. Reproducible code that Copilot helps you write and debug lets you run a reserve review or projection in hours, not weeks - more engagements per year at senior rates.
Python (chainladder, lifelib)GitHub CopilotMicrosoft 365 Copilot
1
Rebuild a recurring deliverable - a loss-reserve triangle or a cash-flow projection - as a Python script using the chainladder or lifelib library, with GitHub Copilot writing the boilerplate while you own the actuarial assumptions.
2
Have the AI scaffold and document the method so it survives peer review.
Copy-paste this prompt
You are a senior actuary. Write documented Python using the chainladder library to load a loss triangle from a CSV, fit a Mack chain-ladder model, produce ultimate estimates with a Mack standard error, and export an exhibit. Add comments explaining each assumption I must review. Use only this synthetic sample data, not real claims.
Great for standing up a reproducible model; swap in client data only inside your firm's approved environment, and reconcile results to your prior method before relying on them.
3
Keep the model in version control so every assumption change is tracked and reviewable - reproducibility is what makes a code-based deliverable defensible under ASOP 56.
What you'll haveReserving and projection work that runs in hours and survives peer review - the throughput that lets you carry more engagements toward top-of-range billings.
2
Lead transparent AI pricing engagements
Why this pays: Pricing sophistication is the highest-value actuarial consulting niche. Being the person who can deploy transparent machine-learning pricing - and defend it to regulators - commands premium rates that push past $277,800.
Akur8hyperexponential (hx Renew)Python (scikit-learn)
1
Learn a transparent pricing platform like Akur8 or hyperexponential that produces GLM and GBM models regulators will accept, rather than black-box output you cannot explain.
2
Benchmark the AI-built model against your traditional GLM and document exactly where and why they differ - clients and regulators pay for the explanation, not just the model.
3
Prepare the regulatory defense before the filing, not after.
Copy-paste this prompt
Act as a pricing actuary. Compare a GLM and a gradient-boosting approach for personal auto rating: list the pros, cons, regulatory-acceptability considerations, and the specific diagnostics I should present to a state insurance regulator to justify each. General methodology only, no client rate tables.
Use to structure your defense of a model; never paste a client's actual rate tables or filings into a consumer tool.
What you'll haveA defensible, faster pricing model and the regulatory narrative to sell it - the specialist positioning behind consulting pay at the top of the range income.
3
Turn model output into client-ready deliverables in a fraction of the time
Why this pays: Clients pay consultants for clarity, not code. Using AI to draft the memo, the board deck, and the plain-English explanation shifts your billable hours toward insight and relationship - the work that earns senior and partner rates.
ClaudeMicrosoft 365 Copilot (PowerPoint)ChatGPT
1
Draft the executive summary from your own sanitized findings.
Copy-paste this prompt
You are an actuarial consultant writing to a non-actuarial insurance board. Turn these bullet findings from a reserve review into a one-page executive summary: [paste your own de-identified findings]. Plain English, lead with the recommendation, flag the two biggest uncertainties, no jargon.
Draft only - verify every number against your workpapers. Keep client identifiers and confidential figures out; paste sanitized findings.
2
Use Copilot in PowerPoint to convert the summary into a board deck, then rewrite the interpretation yourself - the judgment call is the part clients are buying.
What you'll havePolished, plain-English deliverables produced in minutes - more of your day billable to insight, the mix that lifts your effective rate.
4
Build a personal research-and-credentials engine
Why this pays: Credentials (ASA to FSA, ACAS to FCAS) and current technical knowledge directly set your billing rate. AI that compresses exam prep and keeps you current on regulation lets you credential faster and stay the most current person in the room.
NotebookLMPerplexityClaude
1
Load exam syllabi, study notes, and past problems into NotebookLM and generate a queryable study companion plus practice questions with worked solutions.
2
Drill actively instead of re-reading.
Copy-paste this prompt
Act as an actuarial exam tutor for SOA Exam FAM. Build me an 8-week study plan, then quiz me one question at a time on credibility theory, grade my answer, and explain the correct approach. Escalate difficulty as I improve.
Use for active recall; always verify against the official syllabus and study manual - AI can misstate a formula.
3
Keep a Perplexity workflow for tracking regulatory and market changes (NAIC, IFRS 17, new mortality tables) so your advice is always current - a visible differentiator in front of clients.
What you'll haveFaster credentialing and always-current expertise - the technical authority that justifies senior consulting rates.
5
Automate data prep, validation, and experience studies
Why this pays: Most actuarial hours are lost to cleaning and reconciling data. Automating that with AI frees time for judgment and catches the errors that cause model risk - protecting both your reputation and your realization.
Python (pandas)GitHub CopilotMicrosoft 365 Copilot
1
Script your data-validation checks - reconciliations, reasonability tests, outlier flags - in Python with Copilot so every dataset is checked the same way before it hits the model.
2
Generate the validation harness once, reuse it every cycle.
Copy-paste this prompt
Write Python (pandas) to validate an experience-study dataset: check for duplicate policy IDs, negative exposures, impossible dates, and premiums outside expected ranges, then output a data-quality report flagging every exception with an explanation of each check. Use synthetic data only.
Automates the checks; a flagged clean run is not a substitute for your reasonability review of the results.
3
Turn recurring experience studies (mortality, lapse, loss development) into parameterized scripts you rerun each cycle instead of rebuilding by hand.
What you'll haveClean, validated data and repeatable studies with fewer errors - less grunt work, more judgment, and lower model risk.
6
Productize a niche and own AI model governance
Why this pays: The highest-earning independent actuary owns intellectual property and a niche. Packaging a reusable tool, or becoming the person who governs actuarial AI and model risk, turns hours into leverage and premium engagements.
Python (Streamlit)ClaudeExcel
1
Pick a specialty (cyber, climate and catastrophe, pension de-risking, IFRS 17) and build a small reusable tool - a Streamlit app or a templated model - that solves one recurring client problem, with Claude helping you code it.
2
Write the governance playbook your firm and clients don't yet have.
Copy-paste this prompt
Draft a model-validation and governance checklist for an insurer adopting an AI pricing model: data lineage, assumption documentation, benchmark testing, proxy-discrimination review, ongoing monitoring, and sign-off roles, mapped to ASOP 56 considerations. General framework, not legal advice.
A framework to adapt - align it with your firm's standards and applicable regulation before use.
3
Position yourself as the model-governance expert for AI pricing and reserving - the scarce skill as insurers race to adopt these tools.
What you'll haveReusable IP and a governance specialty that command premium, differentiated engagements - the independent path above $277,800.
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
Turn on Copilot in Excel and set up Python with chainladder/lifelib. Rebuild one recurring deliverable as reproducible code and reconcile it to your prior method.
Months 2-3
Add automated data validation and experience-study scripts; start drafting client memos and decks with Claude/Copilot, verifying every number against your workpapers.
Months 3-6
Go deep on a transparent pricing platform (Akur8 or hyperexponential) or a niche specialty; build a personal NotebookLM study-and-research engine to accelerate credentialing.
Months 6-12
Build one reusable tool for your niche and author your firm's AI model-governance playbook - the positioning that supports principal-level rates.
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.

McKinney Python for Data Analysis, 3rd

Same live O’Reilly 3rd already on data-scientist / python-developer / market-research-analyst / quantitative-analyst. This page names Python (pandas) on Automate data prep, validation, and experience studies and the prompt is Write Python (pandas) to validate an experience-study dataset. Not CompTIA Data+ and not leftover Ross Exam P as the lead (that is actuary).

Next steps for an Actuarial Consultant

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.

Actuarial Consultant 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.

Actuarial Consultants 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.

Economics And Accounting programs on Coursera for Actuarial Consultant work

Coursera search for economics and accounting — a professional certificate or bachelor's-level coursework that lines up with computing, not a generic professional-development aisle.

Economics And Accounting courses on edX

edX search for economics and accounting, aimed at computing (SOC 15-2011). Same field as the Coursera link, different university catalog.

Screened remote and flexible Actuarial Consultant listings on FlexJobs

FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Actuarial Consultant work, not a claim that they list a counted SOC 15-2011 inventory.

Build an Actuarial Consultant resume on Resume Now

Write an Actuarial Consultant 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.

Build an Actuarial Consultant resume on Zety

An Actuarial Consultant resume that names the actual tasks on this page, or the step-up title Financial Managers, beats a blank template when you apply.

What Actuarial Consultants 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.
Connecticut$166,800New York$156,480New Jersey$142,800Florida$132,110Wisconsin$131,640California$130,510North Carolina$128,730Iowa$128,690

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.

Frequently asked
Will AI replace actuarial consultants?
No, but it will replace actuaries who only do the spreadsheet mechanics. AI can build and run models; it cannot own an assumption in front of a regulator, sign a statement of actuarial opinion, or carry professional responsibility under the ASOPs. Consultants who use AI to move faster on the plumbing and spend their time on judgment, communication, and client relationships pull ahead; those who guard manual spreadsheet work fall behind.
Is it safe to use ChatGPT or Claude with actuarial data?
Not with identifiable policyholder, claimant, or confidential client data - that belongs only in your firm's approved, contractually covered systems. Use consumer AI for general methods, code scaffolding on synthetic data, exam prep, and drafting from sanitized inputs, and always reconcile AI output to your own workpapers.
Do I still need to pass the exams if AI can do the math?
Yes, more than ever. Credentials (ASA/FSA, ACAS/FCAS) are what let you sign work and set your billing rate, and they signal the judgment AI cannot provide. AI makes you a faster, better-prepared candidate; it does not substitute for the qualification clients and regulators require.
Which AI skill has the biggest payoff for an actuary?
Reproducible, AI-assisted coding in Python. It turns weeks of spreadsheet work into hours of reviewable code, scales across engagements, and is the foundation for pricing machine learning, automated validation, and productized tools - the throughput and specialization that move you toward the top of the range.
Can I trust an AI-built pricing or reserving model?
Only after you validate it. Benchmark it against a method you trust, document every assumption, test for proxy discrimination and instability, and be able to explain it to a regulator. Transparent platforms like Akur8 and hyperexponential exist precisely because black-box output is not defensible. The model can be AI-built; the responsibility is yours.
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.

Sources