US dataswitch to UK
Actuaries
ascertaining premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits, constructing probability tables and providing advice to clients on a contract basis. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: analyzing statistical information to estimate mortality. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, providing advice to clients on a contract basis, stays yours. The tools change hands, the accountability doesn't.
Your week, as this page understands it
Analyze statistical data, such as mortality, accident, sickness, disability, and retirement rates and construct probability tables to forecast risk and liability for payment of future benefits. May ascertain insurance rates required and cash reserves necessary to ensure payment of future benefits. The job title says “actuaries”. The real job is the part underneath: providing advice to clients on a contract basis. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But actuaries is not one task. It is 15 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is providing advice to clients on a contract basis, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 17%
- changing shape
- 60%
- staying human
- 23%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 48 out of 100 (42–54 allowing for uncertainty): partial exposure, across 15 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for actuaries is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
2 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Analyzing statistical information to estimate mortality
This is reading one thing and writing another: statistical information in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Estimating mortality and claim rates from data is textbook statistical modeling with abundant published tables.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Constructing probability tables
This is reading one thing and writing another: probability tables in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Building probability tables from statistical data is exactly what modeling software is designed for.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Changing shape
9 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Ascertaining premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits
The software now makes the first pass at premium rates, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (52–60 allowing for uncertainty): partial exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Rate and reserve calculations follow documented methods, but a qualified actuary normally has to stand behind them.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Collaborating with programmers, underwriters, accounts
The software now makes the first pass at programmers, underwriters, accounts, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Collaborate with programmers, underwriters, accounts, claims experts, and senior management to help companies develop plans for new lines of business or improvements to existing business.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Analysis can be prepared, but shaping a new line of business happens in discussion across teams.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Determining or helping determine, company policy and explaining complex technical matters to company executives, government officials, shareholders, policyholders or the public
The software now makes the first pass at determine, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Technical explanations write well, but persuading executives and regulators happens face to face.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Designing, reviewing and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums
The software now makes the first pass at help administer insurance, annuity and pension plans, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Design, review, and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Plan design and premium math are well documented, though a credentialed actuary normally signs off soundness.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Staying human
4 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Providing advice to clients on a contract basis
The rules require a named, qualified person to answer for advice, and that person cannot be a piece of software.
importance 4 · CoreSource: “Provide advice to clients on a contract basis, working as a consultant.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Consulting income depends on a client trusting this particular adviser’s judgment.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 3/4.
Negotiating terms and conditions of reinsurance with other companies
The value here is that a specific person handles terms and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Negotiate terms and conditions of reinsurance with other companies.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Reinsurance terms are settled through relationships and private market knowledge, not public data.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Testifying before public agencies on proposed legislation affecting businesses
This work happens in the physical world: before public agencies, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Testify before public agencies on proposed legislation affecting businesses.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Testifying before an agency means a credentialed person answering questions live.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 3/4 · needs to be trusted in the moment 3/4 · how much data exists 3/4.
Testifying in court as expert witness or to provide legal evidence on matters
This work happens in the physical world: court, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Testify in court as expert witness or to provide legal evidence on matters such as the value of potential lifetime earnings of a person disabled or killed in an accident.” (O*NET task statement)
How this row was scored
Exposure score: 3 out of 100 (0–7 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Giving evidence in court is legally something only the named expert witness can do.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 4/4 · needs to be trusted in the moment 3/4 · how much data exists 3/4.
Show the other 5 tasks
Determining policy contract provisions for each type of insurance
changing shapeThe software now makes the first pass at policy contract provisions, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Determine policy contract provisions for each type of insurance.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Contract provisions follow standard wordings, but insurers expect a qualified professional to settle them.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Managing credit and help price corporate security offerings
changing shapeThe software now makes the first pass at credit, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 2 · SupplementalSource: “Manage credit and help price corporate security offerings.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Pricing and credit management run on documented models and market data already held digitally.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Determining equitable basis for distributing surplus earnings under participating insurance and annuity contracts in mutual companies
changing shapeThe software now makes the first pass at equitable basis, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Determine equitable basis for distributing surplus earnings under participating insurance and annuity contracts in mutual companies.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Surplus distribution follows documented actuarial method, though a qualified actuary normally has to certify it.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing expertise to help financial institutions manage risks and maximize returns associated with investment products or crediting offerings
changing shapeThe software now makes the first pass at expertise, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Provide expertise to help financial institutions manage risks and maximize returns associated with investment products or credit offerings.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Risk analysis is well documented, but advising an institution depends on an ongoing working relationship.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Explaining changes in contract provisions to customers
changing shapeThe software now makes the first pass at changes, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 2 · SupplementalSource: “Explain changes in contract provisions to customers.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Explaining a contract change is clear writing software handles well, though customers often want to talk it through.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $130,000a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 26,670in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: statistical information in, a record out. The rows above are exactly that shape: analyzing statistical information to estimate mortality and constructing probability tables. What it cannot do is be answerable: advice needs a named person the rules will accept, and software cannot be that person. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Start with what does not change: providing advice to clients on a contract basis is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 17% of this job's task weight sits in rows the software is already learning, 60% in rows that change shape rather than disappear, and 23% in rows it is nowhere near. That is the position, measured across 15 scored tasks. It is not a forecast about you.
So the thing worth your attention is not the job going away. It is the layer around it. Analyzing statistical information to estimate mortality is the part turning into software, and being the person who understands that layer is worth money.
This week: one thing
Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to statistical information, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.
- What you end up holding
- a straight answer about what is actually being rolled out, and when
- How long it takes
- ten minutes
If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether advice is in scope or not. Nothing to log into, no license needed.
Over the next 90 days
Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near ascertaining premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.
Over the next 12 months
On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to actuaries (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was compensation, benefits, and job analysis specialists: only about 3% of its durable work is work you already do and it pays 39.8% less. Your own job splits about 17/83: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “ascertain premium rates required and cash reserves and liabilities necessary to ensure…”, and let the exposed end go.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Compensation, Benefits, and Job Analysis Specialists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already design, review, and help administer insurance, annuity and pension plans, determining financial soundness…, and their equivalent is to administer employee insurance, pension, and savings plans, working with insurance brokers and plan…. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $78,210 against your $130,000, 39.8% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Accountants and Auditors
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already determine, or help determine, company policy, and explain complex technical matters to company…, and their equivalent is to confer with company officials about financial and regulatory matters. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $83,680 against your $130,000, 35.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Financial Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide expertise to help financial institutions manage risks and maximize returns associated with…, and their equivalent is to manage investment funds to maximize return on client investments. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 17% of its task weight, across 15 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like providing advice to clients on a contract basis arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Actuaries, economists and statisticians is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
In UK official statistics this job is counted as Actuaries, economists and statisticians. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for actuaries, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 17% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for actuaries. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for actuaries launches. Nothing else.
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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Actuaries?
- Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Actuaries (United States, SOC 15-2011), 17% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 48 out of 100 (range 42–54, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Actuaries” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates” (88/100, very high); “Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent informa…” (88/100, very high); “Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits” (56/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Actuaries” stay human?
- About 23% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Testify in court as expert witness or to provide legal evidence on matters such as the value of potential lifetime earnings of a person disabled or killed in…” (3/100, minimal); “Testify before public agencies on proposed legislation affecting businesses” (7/100, minimal); “Provide advice to clients on a contract basis, working as a consultant” (18/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Actuaries” do about AI?
- Start from the ledger rather than the headline: 17% of this job's weighted core work is exposed, and roughly 23% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Actuaries calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 15 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
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Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.