US dataswitch to UK
Financial Risk Specialists
drawing charts and graphs, documenting and ensuring communication and analyzing areas of potential risk to the assets. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: drawing charts and graphs, using computer spreadsheets, to illustrate technical reports. The tasks, though, are not you.
It would be a lie to soften that; recommending investments and investment timing is what this work rebuilds around. Your move starts there.
Your week, as this page understands it
Analyze and measure exposure to credit and market risk threatening the assets, earning capacity, or economic state of an organization. May make recommendations to limit risk. The job title says “financial risk specialists”. The real job is the part underneath: recommending investments and investment timing. 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 financial risk specialists is not one task. It is 30 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is recommending investments and investment timing, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 70%
- changing shape
- 20%
- staying human
- 10%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 66 out of 100 (60–72 allowing for uncertainty): high exposure, across 30 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 financial risk specialists 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 2026-08-04. 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.
- O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
- 12 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
21 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.
Drawing charts and graphs, using computer spreadsheets, to illustrate technical reports
This is reading one thing and writing another: charts in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Draw charts and graphs, using computer spreadsheets, to illustrate technical reports.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 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: Making charts and graphs from report data in a spreadsheet is about as routine as computer work gets.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Gathering risk-related data from internal or external resources
This is reading one thing and writing another: risk-related data in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Gather risk-related data from internal or external resources.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 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: Pulling risk data together from internal and external sources is collection work software does quickly.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Tracking, measuring or reporting on aspects of market risk for traded issues
This is reading one thing and writing another: aspects of market risk in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Track, measure, or report on aspects of market risk for traded issues.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 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 and reporting market risk on traded positions is automated measurement and reporting.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Consulting financial literature to ensure use of the latest models or statistical techniques
This is reading one thing and writing another: financial literature in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Consult financial literature to ensure use of the latest models or statistical techniques.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very 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: Keeping up with published models and statistical techniques is literature reading software does thoroughly.
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 0/4 · how much data exists 4/4.
Changing shape
6 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.
Developing or implementing risk-assessment models or methodologies
The software now makes the first pass at risk-assessment models, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Develop or implement risk-assessment models or methodologies.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Designing a risk model needs judgment about what to measure, though the methods are well documented.
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 0/4 · how much data exists 3/4.
Devising systems or processes to monitor validity of risk assessments
The software now makes the first pass at systems, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Devise systems or processes to monitor validity of risk assessments.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Designing checks on whether risk assessments still hold needs judgment about what could quietly go wrong.
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 0/4 · how much data exists 3/4.
Evaluating the risks and benefits involved in implementing green building technologies
The software now makes the first pass at the risks, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Evaluate the risks and benefits involved in implementing green building technologies.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Weighing green building technology needs project-specific detail that general sources do not carry.
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 0/4 · how much data exists 3/4.
Staying human
3 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.
Conferring with traders to identify and communicate risks associated with specific trading strategies or positions
The value here is that a specific person handles traders and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Confer with traders to identify and communicate risks associated with specific trading strategies or positions.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 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: Talking risk through with traders depends on live give-and-take with people who have to act on it.
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 2/4.
Meeting with clients to answer queries on subjects
The value here is that a specific person handles clients and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Meet with clients to answer queries on subjects such as risk exposure, market scenarios, or values-at-risk calculations.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 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: Clients asking about their own exposure want a person who can answer follow-up questions there and then.
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 2/4.
Recommending investments and investment timing
The rules require a named, qualified person to answer for investments, and that person cannot be a piece of software.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Recommend investments and investment timing to companies, investment firm staff, or the public.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 allowing for uncertainty): low 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: Recommending specific investments to others is regulated advice that a licensed person must give.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 3/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Show the other 20 tasks
Evaluating and comparing the relative quality of various securities in a given industry
shifting to AIThis is reading one thing and writing another: the relative quality of various securities in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Evaluate and compare the relative quality of various securities in a given industry.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 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: Comparing securities within an industry uses abundant public data and documented methods.
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 0/4 · how much data exists 4/4.
Monitoring developments in the fields of industrial technology
shifting to AIThis is reading one thing and writing another: developments in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Monitor developments in the fields of industrial technology, business, finance, and economic theory.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very 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: Monitoring developments in business, finance and economic theory is reading and summarizing at scale.
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 0/4 · how much data exists 4/4.
Producing reports or presentations that outline findings
shifting to AIThis is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Produce reports or presentations that outline findings, explain risk positions, or recommend changes.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 allowing for uncertainty): 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: Turning risk findings into reports and slides is writing and charting software does at least as well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Analyzing new legislation to determine impact on risk exposure
shifting to AIThis is reading one thing and writing another: new legislation in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Analyze new legislation to determine impact on risk exposure.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): 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: Reading new legislation and tracing its effect on exposure is document analysis software does quickly.
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 0/4 · how much data exists 3/4.
Conducting statistical analyses to quantify risk
shifting to AIThis is reading one thing and writing another: statistical analyses in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Conduct statistical analyses to quantify risk, using statistical analysis software or econometric models.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 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: Running statistical risk models is software work already, though choosing and validating the model needs expertise.
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 0/4 · how much data exists 3/4.
Evaluating the risks related to green investments
shifting to AIThis is reading one thing and writing another: the risks in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Evaluate the risks related to green investments, such as renewable energy company stocks.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 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: Assessing the risk in renewable energy stocks uses published company and market data.
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 0/4 · how much data exists 3/4.
Documenting and ensuring communication
shifting to AIThis is reading one thing and writing another: communication in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Document, and ensure communication of, key risks.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 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: Writing up and circulating the key risks is documentation work software drafts at least as well.
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 1/4 · how much data exists 3/4.
Analyzing areas of potential risk to the assets
shifting to AIThis is reading one thing and writing another: areas of potential risk in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Analyze areas of potential risk to the assets, earning capacity, or success of organizations.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Mapping what could go wrong for a business is analysis software supports well, though the decisive detail is company-specific.
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 0/4 · how much data exists 2/4.
Developing contingency plans to deal with emergencies
shifting to AIThis is reading one thing and writing another: contingency plans in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Develop contingency plans to deal with emergencies.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Contingency plans follow established templates, but the useful detail is specific to this organization.
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 0/4 · how much data exists 2/4.
Identifying key risks and mitigating factors of potential investments
shifting to AIThis is reading one thing and writing another: key risks in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Identify key risks and mitigating factors of potential investments, such as asset types and values, legal and ownership structures, professional reputations, customer bases, or industry segments.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Spotting the real risks in a deal depends on private detail about the parties and structures involved.
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 0/4 · how much data exists 2/4.
Devising scenario analyses reflecting possible severe market events
shifting to AIThis is reading one thing and writing another: scenario analyses reflecting possible severe market events in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Devise scenario analyses reflecting possible severe market events.” (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: Severe market scenarios are built from published methods and market data software works with well.
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.
Interpreting data on price, yield, stability, future investment-risk trends, economic influences and other factors affecting investment programs
shifting to AIThis is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): 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: Interpreting price, yield and risk data is pattern work over abundant, well documented market information.
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.
Maintaining input or data quality of risk management systems
shifting to AIThis is reading one thing and writing another: input in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Maintain input or data quality of risk management systems.” (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: Keeping risk system data clean and complete is checking work software does reliably.
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.
Preparing plans of action
shifting to AIThis is reading one thing and writing another: plans of action in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Prepare plans of action for investment, using financial analyses.” (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: Investment action plans follow documented methods, though a responsible person signs them off.
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.
Informing financial decisions by analyzing financial information to forecast business
shifting to AIThis is reading one thing and writing another: financial decisions in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Inform financial decisions by analyzing financial information to forecast business, industry, or economic conditions.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 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: Forecasting from financial data is modeling work software does well, with a person owning the call.
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 1/4 · how much data exists 3/4.
Providing statistical modeling advice to other departments
shifting to AIThis is reading one thing and writing another: statistical modeling advice in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Provide statistical modeling advice to other departments.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 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: Advising other departments on modeling is explaining documented methods, with some back-and-forth.
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 1/4 · how much data exists 3/4.
Recommending ways to control or reduce risk
shifting to AIThis is reading one thing and writing another: ways in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Recommend ways to control or reduce risk.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 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: Suggesting ways to reduce risk draws on well-known controls, tailored through conversation.
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 1/4 · how much data exists 3/4.
Reviewing or drafting risk disclosures for offer documents
changing shapeThe software now makes the first pass at risk disclosures, 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 not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Review or draft risk disclosures for offer documents.” (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: Risk disclosures follow well-known formats, though a responsible professional must approve the final wording.
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.
Determining potential environmental impacts of new products or processes on long-term growth and profitability
changing shapeThe software now makes the first pass at potential environmental impacts of new products, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Determine potential environmental impacts of new products or processes on long-term growth and profitability.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Judging environmental impact on long-term profit relies on assumptions specific to the product and market.
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 0/4 · how much data exists 2/4.
Contributing to development of risk management systems
changing shapeThe software now makes the first pass at development of risk management systems, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Contribute to development of risk management systems.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Risk systems can be part designed by software, though shaping them for this organisation needs experience.
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 1/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $117,330a 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
- 63,850in 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: risk-related data in, a record out. The rows above are exactly that shape: drawing charts and graphs, using computer spreadsheets and gathering risk-related data from internal or external resources. What it cannot do is be answerable: investments need 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
The exposed part of your job is the biggest part, and I am not going to dress that up: drawing charts and graphs, using computer spreadsheets, to illustrate technical reports is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 70% of this job's task weight sits in rows the software is already learning, 20% in rows that change shape rather than disappear, and 10% in rows it is nowhere near. That is the position, measured across 30 scored tasks. It is not a forecast about you.
What you have that the software does not is recommending investments and investment timing, 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 risk-related data 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 risk-related data, 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 drawing charts and graphs, using computer spreadsheets, to illustrate technical reports” 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 recommending investments and investment timing 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 financial risk specialists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was financial and investment analysts: only about 10% of its durable work is work you already do and it pays 12.4% less. I am not going to pretend that is comfortable news: 70% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “confer with traders to identify and communicate risks associated with specific trading…” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.
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.
Financial and Investment Analysts
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “recommend investments and investment timing to companies, investment firm staff, or the public”. Across the whole of both lists that adds up to about 10% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 10% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $102,740 against your $117,330, 12.4% 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 recommend investments and investment timing to companies, investment firm staff, or the public, and their equivalent is to manage investment funds to maximize return on client investments. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step.
Personal Financial Advisors
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already prepare plans of action for investment, using financial analyses, and their equivalent is to investigate available investment opportunities to determine compatibility with client financial plans. 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. It is a pay cut, in those words: $105,070 against your $117,330, 10.4% 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: 70% of its task weight, across 30 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: recommending investments and investment timing 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 Finance and investment analysts and advisers 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 Finance and investment analysts and advisers. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Two honest options, and no deadline on either
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
A nearby route
There's no Space built for financial risk specialists yet.
Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.
The closest match is Microsoft 365 Report Builders, a community for people who build business reports in Excel, Power Query and Power BI without a data team behind them. It overlaps with the part of your job that is growing: the reporting layer around risk data. It does not cover risk methodology or regulation. If that overlap isn't you, the free route below covers the same ground.
- Problem: “I’ve been asked to build my first Power BI report, but I only know Excel”
- Problem: “My Monday report takes four hours and managers still ask for last week’s version”
Try Microsoft 365 Report Builders free →
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
After the trial it is a paid community, and you get identical data either way. If the overlap above is not your job, the moves above cost nothing and stand on their own.
Noted, and thank you. We’ll email you if a Space for financial risk specialists launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
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 Financial Risk Specialists?
- Not as a job, but it is already doing parts of the work. Across the 30 official task statements scored for Financial Risk Specialists (United States, SOC 13-2054), 70% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 66 out of 100 (range 60–72, band: high). 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 “Financial Risk Specialists” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Draw charts and graphs, using computer spreadsheets, to illustrate technical reports” (93/100, very high); “Gather risk-related data from internal or external resources” (93/100, very high); “Track, measure, or report on aspects of market risk for traded issues” (93/100, 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 “Financial Risk Specialists” stay human?
- About 10% 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: “Recommend investments and investment timing to companies, investment firm staff, or the public” (25/100, low); “Meet with clients to answer queries on subjects such as risk exposure, market scenarios, or values-at-risk calculations” (35/100, low); “Confer with traders to identify and communicate risks associated with specific trading strategies or positions” (35/100, low). 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 “Financial Risk Specialists” do about AI?
- Start from the ledger rather than the headline: 70% of this job's weighted core work is exposed, and roughly 10% 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 Financial Risk Specialists 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 30 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
About the data on this page
- O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 12 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-with-imputed)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
- 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.