Will AI replace Financial Specialists, All Other?

Not as a job, but it is already doing parts of the work. Across the 44 official task statements scored for Financial Specialists, All Other (United States, SOC 13-2099), 57% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 55 out of 100 (range 50–61, 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 “Financial Specialists, All Other” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Produce written summary reports of financial research results” (93/100, very high); “Develop core analytical capabilities or model libraries, using advanced statistical, quantitative, or econometric techniques” (83/100, very high); “Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss…” (83/100, 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 Specialists, All Other” stay human?

About 27% 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: “Arrest individuals to be charged with fraud” (0/100, minimal); “Conduct field surveillance to gather case-related information” (0/100, minimal); “Testify in court regarding investigation findings” (1/100, 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 “Financial Specialists, All Other” do about AI?

Start from the ledger rather than the headline: 57% of this job's weighted core work is exposed, and roughly 27% 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 Specialists, All Other 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 44 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.

US dataswitch to UK

Financial Specialists, All Other

gathering financial documents related to investigations, researching or developing analytical tools to address issues and maintaining knowledge of current events and trends in such areas as money laundering and criminal tools and techniques. 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: preparing written reports of investigation findings. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 57%
changing shape: 16%
staying human: 27%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 55 out of 100 (50–61 allowing for uncertainty): partial exposure, across 44 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 specialists, all other 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.

It would be a lie to soften that; leading or participating in fraud investigation teams is what this work rebuilds around. The plan below starts there.

Your week, as this page understands it

All financial specialists not listed separately. The job title says “financial specialists” or “all other”: officially one job, two names. The real job is the part underneath: leading or participating in fraud investigation teams. 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 specialists, all other is not one task. It is 44 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is leading or participating in fraud investigation teams, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 57%
changing shape: 16%
staying human: 27%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

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.

7 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

25 tasks

Tasks 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.

Preparing written reports of investigation findings
This is reading one thing and writing another: written reports of investigation findings in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Prepare written reports of investigation findings.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Once the findings exist, writing the investigation report is structured drafting that software does to professional standard.
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 3/4.
Documenting all investigative activities
This is reading one thing and writing another: all investigative activities in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Document all investigative activities.” (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: Case logs follow a set format and can be drafted from notes, but they record what the investigator personally did.
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.
Applying mathematical or statistical techniques to address practical issues in finance
This is reading one thing and writing another: mathematical in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Apply mathematical or statistical techniques to address practical issues in finance, such as derivative valuation, securities trading, risk management, or financial market regulation.” (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: Quantitative finance methods are heavily published, so software can apply them to the firm own data with light checking.
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.
Creating and maintaining logs, records or databases of information about fraudulent activity
This is reading one thing and writing another: logs, records or databases of information in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Create and maintain logs, records, or databases of information about fraudulent activity.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Creating and keeping logs and databases of fraud information is straightforward record-keeping that software handles routinely.
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 3/4.

Changing shape

7 tasks

Tasks 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.

Gathering financial documents related to investigations
The software now makes the first pass at financial documents, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Gather financial documents related to investigations.” (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: Pulling together the documents is largely systems work, though some records must be requested formally or collected in person.
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.
Recommending actions in fraud cases
The software now makes the first pass at actions, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Recommend actions in fraud cases.” (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: Recommending next steps follows established fraud-case patterns, so a solid draft recommendation can be produced from the file.
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.
Preparing evidence for presentation in court
The software now makes the first pass at evidence, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Prepare evidence for presentation in court.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 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: Organizing and presenting evidence follows court rules that are well documented, though exhibits must be physically prepared.
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 0/4 · how much data exists 3/4.

Staying human

12 tasks

Tasks 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.

Leading or participating in fraud investigation teams
The value here is that a specific person handles fraud investigation teams and stands behind it. That is earned, not computed.
importance 5 · Core
Source: “Lead, or participate in, fraud investigation teams.” (O*NET task statement)
How this row was scored
Exposure score: 12 out of 100 (8–16 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Leading an investigation team means people following a person direction and carrying responsibility for the case.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Interviewing witnesses or suspects and taking statements
This work happens in the physical world: witnesses, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Interview witnesses or suspects and take statements.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Getting a witness or suspect to talk depends on a person in the room reading them and earning cooperation.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Coordinating investigative efforts with law enforcement officers and attorneys
The value here is that a specific person handles investigative efforts and stands behind it. That is earned, not computed.
importance 5 · Core
Source: “Coordinate investigative efforts with law enforcement officers and attorneys.” (O*NET task statement)
How this row was scored
Exposure score: 12 out of 100 (8–16 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Working a case alongside police and lawyers runs on trust and judgment built between named professionals.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.

Show the other 34 tasks

Producing written summary reports of financial research results
shifting to AI
This is reading one thing and writing another: written summary reports of financial research results in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Produce written summary reports of financial research results.” (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: Once the results exist, writing them up clearly is exactly the kind of drafting software does at professional quality.
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.
Developing core analytical capabilities or model libraries
shifting to AI
This is reading one thing and writing another: core analytical capabilities in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop core analytical capabilities or model libraries, using advanced statistical, quantitative, or econometric techniques.” (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: Building statistical model libraries is coding against published techniques, an area where automated tools are already strong.
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.
Researching or developing analytical tools to address issues
shifting to AI
This is reading one thing and writing another: analytical tools in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss measurement, or pricing models.” (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: Portfolio and pricing tools are built from well-documented methods and code, which software now writes to a usable standard.
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.
Researching or evaluating new technologies for use in fraud detection systems
shifting to AI
This is reading one thing and writing another: new technologies in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Research or evaluate new technologies for use in fraud detection systems.” (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: Researching and comparing new fraud detection technology is reading and evaluating published material, which tools do 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 4/4.
Designing, implementing or maintaining fraud detection tools or procedures
shifting to AI
This is reading one thing and writing another: fraud detection tools in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Design, implement, or maintain fraud detection tools or procedures.” (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: Fraud detection rules and tools are technical builds from published methods, an area automated tools handle strongly.
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.
Analyzing financial data to detect irregularities in areas
shifting to AI
This is reading one thing and writing another: financial data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze financial data to detect irregularities in areas such as billing trends, financial relationships, and regulatory compliance procedures.” (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: Spotting odd patterns in billing and financial data is number-crunching computers do faster and more consistently than people.
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.
Developing methods of assessing or measuring corporate performance in terms of environmental
shifting to AI
This is reading one thing and writing another: methods in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Develop methods of assessing or measuring corporate performance in terms of environmental, social, and governance (ESG) issues.” (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: Environmental and governance measurement frameworks are widely published, so drafting a method is well within automated reach.
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.
Identifying, tracking or maintaining metrics for trading system operations
shifting to AI
This is reading one thing and writing another: metrics in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Identify, track, or maintain metrics for trading system operations.” (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: Defining and tracking operational metrics is standard data work that software builds and maintains reliably.
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.
Researching new financial products or analytics to determine their usefulness
shifting to AI
This is reading one thing and writing another: new financial products in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Research new financial products or analytics to determine their usefulness.” (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: Researching new products and judging their usefulness is reading and analysis software does quickly, with firm context added.
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.
Maintaining or modifying all financial analytic models
shifting to AI
This is reading one thing and writing another: all financial analytic models in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Maintain or modify all financial analytic models in use.” (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: Updating and adjusting existing models is code and documentation work that automated tools handle with review.
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.
Interpreting results of financial analysis procedures
shifting to AI
This is reading one thing and writing another: results of financial analysis procedures in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Interpret results of financial analysis procedures.” (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: Explaining what the numbers mean is written analysis software does well, though firm-specific judgment still shapes conclusions.
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.
Reviewing reports of suspected fraud to determine need for further investigation
shifting to AI
This is reading one thing and writing another: reports of suspected fraud in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review reports of suspected fraud to determine need for further investigation.” (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: Triaging incoming fraud reports against known warning signs is exactly the sort of screening 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.
Analyzing pricing or risks of carbon trading products
shifting to AI
This is reading one thing and writing another: risks of carbon trading products in, a record out. That is the shape today's tools are built for.
importance 2 · Supplemental
Source: “Analyze pricing or risks of carbon trading products.” (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: Carbon market pricing and risk analysis is numerical work on published market data, well suited to automated modeling.
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.
Assessing the potential impact of climate change on business financial issues
shifting to AI
This is reading one thing and writing another: the potential impact of climate change in, a record out. That is the shape today's tools are built for.
importance 2 · Supplemental
Source: “Assess the potential impact of climate change on business financial issues, such as damage repairs, insurance costs, or potential disruptions of daily activities.” (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: Climate impact assessments follow published scenarios and methods, so a solid draft can be produced from company 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.
Developing tools to assess green technologies or green financial products
shifting to AI
This is reading one thing and writing another: tools in, a record out. That is the shape today's tools are built for.
importance 2 · Supplemental
Source: “Develop tools to assess green technologies or green financial products, such as green hedge funds or social responsibility investment funds.” (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: Building assessment tools for green products is documented analytical work software produces to a usable standard.
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.
Devising, or applying independenting models or tools to help verify results of analytical systems
shifting to AI
This is reading one thing and writing another: models in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Devise or apply independent models or tools to help verify results of analytical systems.” (O*NET task statement)
How this row was scored
Exposure score: 72 out of 100 (65–79 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: Independent checking models can be built automatically, but firms deliberately keep a separate human answerable for validation.
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 4/4.
Collaborating in the development or testing of new analytical software to ensure compliance with user requirements
shifting to AI
This is reading one thing and writing another: the development in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Collaborate in the development or testing of new analytical software to ensure compliance with user requirements, specifications, or scope.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 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: Testing software against written requirements is highly automatable, though agreeing scope involves working with other people.
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 4/4.
Defining or recommending model specifications or data collection methods
shifting to AI
This is reading one thing and writing another: model specifications in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Define or recommend model specifications or data collection methods.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 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: Model specifications and data collection plans are documented craft, though the final choice is agreed with colleagues.
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 4/4.
Maintaining knowledge of current events and trends in such areas as money laundering and criminal tools and techniques
shifting to AI
It is the same call made over and over on knowledge of current events, with a right answer to check it against. That is what a model is trained on.
importance 4 · Core
Source: “Maintain knowledge of current events and trends in such areas as money laundering and criminal tools and techniques.” (O*NET task statement)
How this row was scored
Exposure score: 65 out of 100 (58–72 allowing for uncertainty): high 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: Monitoring published trends is easy to automate, but the task is the investigator personally staying current.
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 4/4.
Preparing requirements documentation for use by software developers
shifting to AI
This is reading one thing and writing another: requirements documentation in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Prepare requirements documentation for use by software developers.” (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: Requirements documents are structured writing software drafts well once the needs are gathered.
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 application or analytical support to researchers or traders on issues
shifting to AI
This is reading one thing and writing another: application in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Provide application or analytical support to researchers or traders on issues such as valuations or data.” (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: Answering colleagues valuation and data questions is well-defined technical support that software can largely produce.
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.
Advising businesses or agencies on ways to improve fraud detection
changing shape
The software now makes the first pass at businesses, 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 · Core
Source: “Advise businesses or agencies on ways to improve fraud detection.” (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: Advice on improving fraud controls is well-documented material, though persuading a client to act is a live 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 2/4 · how much data exists 3/4.
Evaluating business operations to identify risk areas for fraud
changing shape
The software now makes the first pass at business operations, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Evaluate business operations to identify risk areas for fraud.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 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: Reviewing how a business operates to spot fraud risk is analytical, though it usually involves visiting and asking staff.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Collaborating with product development teams
changing shape
The software now makes the first pass at product development teams, 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 · Supplemental
Source: “Collaborate with product development teams to research, model, validate, or implement quantitative structured solutions for new or expanded markets.” (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: Modeling can be automated, but working alongside product teams on new markets depends on shared, in-person problem solving.
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.
Developing solutions to help clients hedge carbon exposure or risk
changing shape
The software now makes the first pass at solutions, 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 · Supplemental
Source: “Develop solutions to help clients hedge carbon exposure or risk.” (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: Hedging structures can be modeled automatically, but designing one for a client involves understanding their situation through discussion.
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.
Training others in fraud detection and prevention techniques
staying human
The value here is that a specific person handles this work and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Train others in fraud detection and prevention techniques.” (O*NET task statement)
How this row was scored
Exposure score: 34 out of 100 (27–41 allowing for uncertainty): low 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: Training material writes itself easily now, but delivering training that changes behavior happens with a person in front of people.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 4/4.
Conferring with other financial engineers or analysts on trading strategies
staying human
The value here is that a specific person handles other financial engineers and stands behind it. That is earned, not computed.
importance 3 · Core
Source: “Confer with other financial engineers or analysts on trading strategies, market dynamics, or trading system performance to inform development of quantitative techniques.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (24–32 allowing for uncertainty): low exposure, high 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: Trading ideas get shaped in live discussion among specialists, where reading colleagues matters as much as the analysis.
The five ratings: output a model can produce 1/4 · 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.
Conducting in-depth investigations of suspicious financial activity
staying human
The rules require a named, qualified person to answer for in-depth investigations of suspicious financial activity, and that person cannot be a piece of software.
importance 4 · Core
Source: “Conduct in-depth investigations of suspicious financial activity, such as suspected money-laundering efforts.” (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: someone qualified has to answer for it.
The rating behind it: Deep money-laundering investigations follow evidential rules and need investigator judgement, though data screening is heavily tool-assisted.
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 1/4 · how much data exists 2/4.
Consulting traders or other financial industry personnel to determine the need for new or improved analytical applications
staying human
The value here is that a specific person handles traders and stands behind it. That is earned, not computed.
importance 3 · Core
Source: “Consult traders or other financial industry personnel to determine the need for new or improved analytical applications.” (O*NET task statement)
How this row was scored
Exposure score: 23 out of 100 (19–27 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Finding out what traders actually need comes from conversations and watching how they work, not from documents.
The five ratings: output a model can produce 1/4 · 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.
Negotiating with responsible parties to arrange for recovery of losses due to fraud
staying human
The value here is that a specific person handles responsible parties and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Negotiate with responsible parties to arrange for recovery of losses due to fraud.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (12–20 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Recovering losses depends on bargaining with a real counterparty who is reading the negotiator across the table.
The five ratings: output a model can produce 1/4 · 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.
Obtaining and serving subpoenas
staying human
This work happens in the physical world: subpoenas, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Obtain and serve subpoenas.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (4–18 allowing for uncertainty): minimal exposure, medium 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 rating behind it: The paperwork can be drafted automatically, but serving a subpoena means physically delivering it to a named person.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Testifying in court regarding investigation findings
staying human
This work happens in the physical world: court regarding investigation findings, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Testify in court regarding investigation findings.” (O*NET task statement)
How this row was scored
Exposure score: 1 out of 100 (0–5 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: 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 under oath is something only the named person who did the work can lawfully do.
The five ratings: output a model can produce 0/4 · 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 1/4.
Conducting field surveillance to gather case-related information
staying human
This work happens in the physical world: field surveillance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Conduct field surveillance to gather case-related information.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Field surveillance means physically watching a place or person, which cannot be done from a screen.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Arresting individuals to be charged with fraud
staying human
This work happens in the physical world: individuals, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Arrest individuals to be charged with fraud.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: An arrest is a legal power carried out in person and cannot be done from a screen.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 4/4 · needs to be trusted in the moment 1/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay: $81,100a 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: 132,130in 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: written reports of investigation findings in, a record out. The rows above are exactly that shape: preparing written reports of investigation findings and documenting all investigative activities. What it cannot do is be trusted in person, which is what fraud investigation teams run on: someone specific doing it and standing behind it. 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: preparing written reports of investigation findings is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 57% of this job's task weight sits in rows the software is already learning, 16% in rows that change shape rather than disappear, and 27% in rows it is nowhere near. That is the position, measured across 44 scored tasks. It is not a forecast about you.

What you have that the software does not is leading or participating in fraud investigation teams, 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 written reports of investigation findings 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 written reports of investigation findings, 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 preparing written reports of investigation findings” 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 leading or participating in fraud investigation teams 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 specialists, all other (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was detectives and criminal investigators: only about 9% of its durable work is work you already do. Your own job splits about 57/43: 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 “gather financial documents related to investigations”, 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.

Detectives and Criminal Investigators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already prepare evidence for presentation in court, and their equivalent is to testify in court and present evidence. 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.
Look at that job’s page anyway →
Forensic Science Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already testify in court regarding investigation findings, and their equivalent is to testify in court about investigative or analytical methods or findings. 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: $72,060 against your $81,100, 11.1% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 19,120 of those jobs against 132,130 of yours (OEWS May 2025), 14% as many seats.
Look at that job’s page anyway →
Securities, Commodities, and Financial Services Sales Agents
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already interpret results of financial analysis procedures, and their equivalent is to develop financial plans, based on analysis of clients' financial status. 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: 56% of its own task list already scores in the top exposure band (62/100 in this release), so the same software is eating it.
Look at that job’s page anyway →

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: 57% of its task weight, across 44 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: leading or participating in fraud investigation teams 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 Managers and directors in the creative industries 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 Managers and directors in the creative industries, Protective service associate professionals n.e.c., Chartered and certified accountants, Estate agents and auctioneers and Sales related 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:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for financial specialists / all other, 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 57% of the work on this page is already inside what they can do.

Try The AI Authority free →

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 financial specialists / all other. 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 financial specialists / all other 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 Space for financial specialists / all other yet. Should there be one?

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.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for financial specialists / all other existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for financial specialists / all other launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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 Specialists, All Other?: Not as a job, but it is already doing parts of the work. Across the 44 official task statements scored for Financial Specialists, All Other (United States, SOC 13-2099), 57% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 55 out of 100 (range 50–61, 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 “Financial Specialists, All Other” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Produce written summary reports of financial research results” (93/100, very high); “Develop core analytical capabilities or model libraries, using advanced statistical, quantitative, or econometric techniques” (83/100, very high); “Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss…” (83/100, 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 Specialists, All Other” stay human?: About 27% 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: “Arrest individuals to be charged with fraud” (0/100, minimal); “Conduct field surveillance to gather case-related information” (0/100, minimal); “Testify in court regarding investigation findings” (1/100, 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 “Financial Specialists, All Other” do about AI?: Start from the ledger rather than the headline: 57% of this job's weighted core work is exposed, and roughly 27% 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 Specialists, All Other 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 44 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.
7 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 2026-08-04.
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.

How we score a job Download this release Look up another job

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}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. 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.