US dataswitch to UK
Brokerage Clerks
corresponding with customers and conferring with coworkers to answer inquiries, scheduling and coordinating transfer and delivery of security certificates between companies and monitoring daily stock prices and computing fluctuations to determine the need for additional collateral to secure loans. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: documenting security transactions, such as purchases, sales, conversions, redemptions or payments, using computers, accounting ledgers or certificate records is work AI now does quickly and cheaply, and performing clerical tasks, such as answering phones or distributing mail is work it can't touch.
Which half fills your week decides your exposure. The ledger below shows which rows you can move toward.
Your week, as this page understands it
Perform duties related to the purchase, sale, or holding of securities. Duties include writing orders for stock purchases or sales, computing transfer taxes, verifying stock transactions, accepting and delivering securities, tracking stock price fluctuations, computing equity, distributing dividends, and keeping records of daily transactions and holdings. The job title says “brokerage clerks”. The real job is the part underneath: performing clerical tasks, such as answering phones or distributing mail. 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 brokerage clerks is not one task. It is 10 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is performing clerical tasks, such as answering phones or distributing mail, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 47%
- changing shape
- 26%
- staying human
- 28%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 59 out of 100 (54–65 allowing for uncertainty): partial exposure, across 10 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 brokerage clerks 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.
- 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
6 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.
Documenting security transactions, such as purchases, sales, conversions, redemptions or payments, using computers, accounting ledgers or certificate records
This is reading one thing and writing another: security transactions in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Document security transactions, such as purchases, sales, conversions, redemptions, or payments, using computers, accounting ledgers, or certificate records.” (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: Logging trades and settlements into systems is exactly the sort of structured record-keeping software already handles.
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.
Preparing forms, such as receipts, withdrawal orders, transmittal papers or transferring confirmations
This is reading one thing and writing another: forms in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Prepare forms, such as receipts, withdrawal orders, transmittal papers, or transfer confirmations, based on transaction requests from stockholders.” (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: Filling standard transfer and receipt forms from a transaction request is routine template work.
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.
Preparing reports summarizing daily transactions and earnings for individual customer accounts
This is reading one thing and writing another: reports summarizing daily transactions in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Prepare reports summarizing daily transactions and earnings for individual customer accounts.” (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: Summarizing the day's transactions and earnings per account is standard report generation from existing data.
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.
Computing total holdings, dividends, interest, transfer taxes, brokerage fees or commissions and allocate appropriate payments to customers
This is reading one thing and writing another: total holdings, dividends, interest, transfer taxes in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Compute total holdings, dividends, interest, transfer taxes, brokerage fees, or commissions and allocate appropriate payments to customers.” (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: Working out dividends, fees and commissions from holdings is exactly the kind of calculation software does reliably.
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.
Verifying ownership and transaction information and dividend distribution instructions to ensure conformance with governmental regulations
This is reading one thing and writing another: ownership in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Verify ownership and transaction information and dividend distribution instructions to ensure conformance with governmental regulations, using stock records and reports.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high 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: Cross-checking ownership and dividend instructions against records is highly automatable, though unusual cases still need a person.
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 0/4 · how much data exists 3/4.
Monitoring daily stock prices and computing fluctuations to determine the need for additional collateral to secure loans
This is reading one thing and writing another: stock prices in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Monitor daily stock prices and compute fluctuations to determine the need for additional collateral to secure loans.” (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: Tracking prices and calculating whether more collateral is needed is straightforward arithmetic on live market data.
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
2 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.
Corresponding with customers and conferring with coworkers to answer inquiries
The software now makes the first pass at customers, 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 5 · CoreSource: “Correspond with customers and confer with coworkers to answer inquiries, discuss market fluctuations, or resolve account problems.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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: Drafting clear answers about accounts and markets is easy, but reassuring an anxious customer still helps to be human.
The five ratings: output a model can produce 3/4 · 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.
Scheduling and coordinating transfer and delivery of security certificates between companies
The software now makes the first pass at transfer, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Schedule and coordinate transfer and delivery of security certificates between companies, departments, and customers.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 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: The scheduling and paperwork are easy to automate, though the certificates themselves still have to physically move.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Staying human
2 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.
Performing clerical tasks, such as answering phones or distributing mail
This work happens in the physical world: clerical tasks, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Perform clerical tasks, such as answering phones or distributing mail.” (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 same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Phone answering can be automated, but sorting and carrying mail around the office cannot.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Filing type or operating standard office machines
This work happens in the physical world: type, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “File, type, or operate standard office machines.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Typing is easily automated, but pulling paper files and running office machines needs hands.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $65,750a 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
- 35,940in 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: security transactions in, a record out. The rows above are exactly that shape: documenting security transactions, such as purchases, sales and preparing forms, such as receipts, withdrawal orders, transmittal papers or transferring confirmations. What it cannot do is be there in the room, and that is still where clerical tasks get done. 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
The exposed part of your job is the biggest part, and I am not going to dress that up: documenting security transactions, such as purchases, sales, conversions, redemptions or payments, using computers, accounting ledgers or certificate records is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 47% of this job's task weight sits in rows the software is already learning, 26% in rows that change shape rather than disappear, and 28% in rows it is nowhere near. That is the position, measured across 10 scored tasks. It is not a forecast about you.
What you have that the software does not is performing clerical tasks, such as answering phones or distributing mail, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of security transactions you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of security transactions, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is documenting security transactions, such as purchases, sales, conversions, redemptions or payments, using computers, accounting ledgers or certificate records” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of performing clerical tasks, such as answering phones or distributing mail you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. 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 brokerage clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was secretaries and administrative assistants, except legal, medical, and executive: only about 2% of its durable work is work you already do, it is under the same pressure this job is and it pays 27.7% less. Your own job splits about 47/53: 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 “correspond with customers and confer with coworkers to answer inquiries, discuss market…”, 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.
Secretaries and Administrative Assistants, Except Legal, Medical, and Executive
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already file, type, or operate standard office machines, and their equivalent is to learn to operate new office technologies as they are developed and implemented. 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. I will not move you off one melting floe onto another: 58% of its own task list already scores in the top exposure band (61/100 in this release), so the same software is eating it. It is a pay cut, in those words: $47,540 against your $65,750, 27.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Credit Analysts
Why it looked obvious: It came up as a near neighbour on the overall shape of the two task lists, but nothing in your day matched a specific piece of theirs closely enough to name.
Why I am not recommending it: The two task lists look alike from a distance and share almost nothing close up: no single piece of their work matched a piece of yours. That is a resemblance, not a route. I will not move you off one melting floe onto another: 78% of its own task list already scores in the top exposure band (70/100 in this release), so the same software is eating it.
Loan Interviewers and Clerks
Why it looked obvious: It came up as a near neighbour on the overall shape of the two task lists, but nothing in your day matched a specific piece of theirs closely enough to name.
Why I am not recommending it: The two task lists look alike from a distance and share almost nothing close up: no single piece of their work matched a piece of yours. That is a resemblance, not a route. It is a pay cut, in those words: $50,020 against your $65,750, 23.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
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: 47% of its task weight, across 10 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: performing clerical tasks, such as answering phones or distributing mail is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
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 Pensions and insurance clerks and assistants 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
The other groups this work is counted across:
In UK official statistics this job is counted as Pensions and insurance clerks and assistants, Finance officers and Financial administrative occupations n.e.c.. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
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 brokerage clerks, 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 47% 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 brokerage clerks. 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 brokerage clerks 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 Brokerage Clerks?
- Not as a job, but it is already doing parts of the work. Across the 10 official task statements scored for Brokerage Clerks (United States, SOC 43-4011), 47% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 59 out of 100 (range 54–65, 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 “Brokerage Clerks” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Prepare forms, such as receipts, withdrawal orders, transmittal papers, or transfer confirmations, based on transaction requests from stockholders” (88/100, very high); “Monitor daily stock prices and compute fluctuations to determine the need for additional collateral to secure loans” (88/100, very high); “Compute total holdings, dividends, interest, transfer taxes, brokerage fees, or commissions and allocate appropriate payments to customers” (88/100, very high). 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 “Brokerage Clerks” stay human?
- About 28% 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: “Perform clerical tasks, such as answering phones or distributing mail” (24/100, low); “File, type, or operate standard office machines” (29/100, low); “Schedule and coordinate transfer and delivery of security certificates between companies, departments, and customers” (42/100, partial). 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 “Brokerage Clerks” do about AI?
- Start from the ledger rather than the headline: 47% of this job's weighted core work is exposed, and roughly 28% 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 Brokerage Clerks 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 10 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.
- 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
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
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.