Financial Risk Specialists
In short: today's best AI models could do about 46% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 92% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 8% of the time, stays with people.
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.
63,850 people do this job in the US. $117,330 a year.
Reviewed on October 2, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on0 h
- A person checks AI's work18 h
- Desk work AI can't do yet19 h
- Stays with people3 h
Financial Risk Specialists, now: of a 40-hour week, AI could take on 0 hours, 18 hours become checking its work, 19 hours are desk work it can't do yet, and 3 hours stay with people.
The largest pieces: Analyze risks to the organization (about 1 h, a person checks); Evaluate green building technologies (under an hour, a person checks); Prepare investment action plans (under an hour, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 90%
- 3%
- 7%
- 1%
- 0%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 46% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 5%. Today's estimate: 46%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.36% went to this job's tasks in May 2026, the 72nd largest of the 447 jobs that showed up at all; 10 of its 30 tasks showed up. Within each of Anthropic's methods, never across a change: 0.2% to 0.16% (released February and March 2025); 0.068% to 0.093% (August 2025 to February 2026); 0.36% to 0.36% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.2% | 91 of 708 | 17 of 30 |
| Released March 2025 | 0.16% | 111 of 703 | 14 of 30 |
| The week of Aug 4, 2025 (released September 2025) | 0.068% | 197 of 655 | 10 of 30 |
| The week of Nov 13, 2025 (released January 2026) | 0.065% | 204 of 672 | 11 of 30 |
| The week of Feb 5, 2026 (released March 2026) | 0.093% | 173 of 677 | 13 of 30 |
| April 2026 (released June 2026) | 0.36% | 70 of 434 | 9 of 30 |
| May 2026 (released June 2026) | 0.36% | 72 of 447 | 10 of 30 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates almost none of this job's working time as physical work, so robots do not reach it.
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
6% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Help build risk management systems, the conversation, 2%
- Develop contingency plans, the conversation, 1%
- Report on risk positions, the conversation, 0.7%
- Answer clients' risk questions, the conversation, 0.6%
- Advise departments on modeling, the conversation, 0.6%
- Recommend investments and timing, the conversation, 0.6%
What becomes checking the machine
90% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 1% is desk work that is small if wrong, which can be handed over with lighter checks.
- Develop risk assessment models, severe if wrong, 10%
- Design checks on risk assessments, severe if wrong, 7%
- Develop contingency plans, severe if wrong, 5%
- Evaluate green building technologies, costly if wrong, 5%
- Assess environmental effects on profitability, costly if wrong, 5%
- Identify risks in potential investments, severe if wrong, 5%
What to learn
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check or sign: spotting what a draft gets wrong is a skill of its own.
- Briefing a model and checking its drafts. About 90% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 30 tasks from .
| Task | ||||
|---|---|---|---|---|
| Develop or implement risk-assessment models or methodologies.Building the models and methods and putting them into use. AI observed on this task. | 10% | 2.5 work days6.7 h to 1.7 work weeks | Documents 100% | 18%Half by Jun 2027 |
| Contribute to development of risk management systems.Specifying and testing the risk systems with the developers. | 7% | 1.7 work days6.7 h to 1.3 work weeks | Documents 70%, conversation 30% | 26%Desk part half by Jan 2027 |
| Develop contingency plans to deal with emergencies.Writing the plans for market and operational emergencies, agreed with the teams. | 7% | 1.7 work days3.3 h to 1.3 work weeks | Documents 80%, conversation 20% | 19%Desk part half by May 2027 |
| Devise systems or processes to monitor validity of risk assessments.Designing the back tests and reviews that show whether the risk numbers hold. | 7% | 1.7 work days3.3 h to 1.3 work weeks | Documents 100% | 24%Half by May 2027 |
| Conduct statistical analyses to quantify risk, using statistical analysis software or econometric models.Running the statistical and econometric models. | 5% | 1.3 work days2.5 h to 4.2 work days | Documents 100% | 36%Half by Feb 2027 |
| Determine potential environmental impacts of new products or processes on long-term growth and profitability.Analysis of what a new product's environmental effects could cost over time. | 5% | 1.3 work days3.3 h to 4.2 work days | Documents 100% | 44%Half by Dec 2026 |
| Devise scenario analyses reflecting possible severe market events.Designing the stress scenarios and running them through the models. | 5% | 1.3 work days2.5 h to 4.2 work days | Documents 100% | 36%Half by Feb 2027 |
| Evaluate the risks and benefits involved in implementing green building technologies.Weighing the costs, risks and returns of green building choices. | 5% | 1.3 work days3.3 h to 3.8 work days | Documents 100% | 47%Half by Nov 2026 |
| 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.Analysis of each investment's structure, values, people and market for what could go wrong. | 5% | 1.3 work days3.3 h to 4.2 work days | Documents 100% | 44%Half by Dec 2026 |
| Inform financial decisions by analyzing financial information to forecast business, industry, or economic conditions.Analysis of the figures to forecast where the business and the economy go. AI observed on this task. | 5% | 1.3 work days3.3 h to 4.2 work days | Documents 100% | 44%Half by Dec 2026 |
| Evaluate the risks related to green investments, such as renewable energy company stocks.Analysis of the risks in renewable energy and other green holdings. | 4% | 1 work day2.5 h to 3.1 work days | Documents 100% | 52%Half by now |
| Analyze areas of potential risk to the assets, earning capacity, or success of organizations.Analysis of where the organization's assets and earnings are exposed. | 3% | 6.7 h3.3 h to 1.7 work days | Documents 100% | 92%Half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Financial Quantitative Analysts25% now, 90% by 2028
- Financial and Investment Analysts53% now, 84% by 2028
- Investment Fund Managers54% now, 89% by 2028
- Personal Financial Advisors71% now, 73% by 2028
- Management Analysts37% now, 85% by 2028
- Securities, Commodities, and Financial Services Sales Agents69% now, 73% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
- Credit Intermediation and Related Activities (5221 and 5223 only)17,810 people
- Securities, Commodity Contracts, and Other Financial Investments and Related Activities9,840 people
- Management of Companies and Enterprises6,700 people
- Nondepository Credit Intermediation4,670 people
- Insurance Carriers4,010 people
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Financial Risk Specialists is within reach of AI now?
- An estimated 46% of the working time is within reach of AI models now and 92% by the end of 2028, on METR's long-run pace at four in five (84% to 93% across the three paces the site shows). Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Financial Risk Specialists come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Analyze risks to the organization (92% of the task's time now, 100% by the end of 2028); Evaluate green building technologies (47% of the task's time now, 100% by the end of 2028); and Prepare investment action plans (69% of the task's time now, 100% by the end of 2028).
- Which tasks of Financial Risk Specialists stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Answer clients' risk questions. Together such tasks are about 1% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Financial Risk Specialists learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are reviewing work you did not do yourself; briefing a model and checking its drafts; and judgment where errors are severe. By the end of 2028 on the long-run pace at four in five, about 6% of the working time stays with people, 90% becomes checking a model's work and 1% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Financial Risk Specialists?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.36% went to this job's tasks, the 72nd largest share of the 447 jobs that showed up; 10 of its 30 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 26%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Financial Risk Specialists?
- Anthropic's robot exposure rating (September 2026) finds almost none of the working time is physical work, so robots do not reach it.
- How is this estimated?
- From the 30 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on October 2, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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