Financial Quantitative Analysts
In short: today's best AI models could do about 25% 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 90% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 5% of the time, stays with people.
Develop quantitative techniques to inform securities investing, equities investing, pricing, or valuation of financial instruments. Develop mathematical or statistical models for risk management, asset optimization, pricing, or relative value analysis.
$81,100 a year (for the broader BLS group this job sits in).
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 work10 h
- Desk work AI can't do yet28 h
- Stays with people2 h
Financial Quantitative Analysts, now: of a 40-hour week, AI could take on 0 hours, 10 hours become checking its work, 28 hours are desk work it can't do yet, and 2 hours stay with people.
The largest pieces: Apply mathematics to finance problems (about 3 h, a person checks); Interpret analysis results (about 1 h, a person checks); Build independent checks on the systems (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.
- 94%
- 1%
- 5%
- 0%
- 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 25% 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, 3%. Today's estimate: 25%.
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, 0.21% went to this job's tasks in May 2026, the 100th largest of the 447 jobs that showed up at all; 4 of its 21 tasks showed up. Within each of Anthropic's methods, never across a change: 0.42% to 0.3% (released February and March 2025); 0.16% to 0.19% (August 2025 to February 2026); 0.23% to 0.21% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.42% | 47 of 708 | 10 of 21 |
| Released March 2025 | 0.3% | 62 of 703 | 10 of 21 |
| The week of Aug 4, 2025 (released September 2025) | 0.16% | 112 of 655 | 10 of 21 |
| The week of Nov 13, 2025 (released January 2026) | 0.13% | 125 of 672 | 12 of 21 |
| The week of Feb 5, 2026 (released March 2026) | 0.19% | 94 of 677 | 12 of 21 |
| April 2026 (released June 2026) | 0.23% | 90 of 434 | 3 of 21 |
| May 2026 (released June 2026) | 0.21% | 100 of 447 | 4 of 21 |
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
4% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Test new analytical software, the conversation, 2%
- Model structured products with product teams, the conversation, 1%
- Confer with analysts on strategies, the conversation, 0.6%
What becomes checking the machine
89% 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.
- Apply mathematics to finance problems, severe if wrong, 16%
- Build model libraries, severe if wrong, 16%
- Build independent checks on the systems, severe if wrong, 15%
- Develop portfolio and pricing tools, severe if wrong, 11%
- Test new analytical software, costly if wrong, 5%
- Specify models and data methods, severe if wrong, 4%
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: spotting what a draft gets wrong is a skill of its own.
- Briefing a model and checking its drafts. About 94% 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 . 21 tasks from .
| Task | ||||
|---|---|---|---|---|
| Apply mathematical or statistical techniques to address practical issues in finance, such as derivative valuation, securities trading, risk management, or financial market regulation.Working the mathematics of valuation, trading and risk at the desk.Done more than monthly by most who do it. AI observed on this task. | 16% | 6.7 h1.7 h to 4.2 work days | Documents 100% | 40%Half by Jan 2027 |
| Devise or apply independent models or tools to help verify results of analytical systems.Building a second model to check the first one's answers.Done more than yearly by most who do it. | 16% | 2.1 work days3.3 h to 1.7 work weeks | Documents 100% | 15%Half by Oct 2027 |
| Develop core analytical capabilities or model libraries, using advanced statistical, quantitative, or econometric techniques.Writing the core model code.Done more than monthly by most who do it. | 16% | 4.2 work days1.7 work days to 3.5 work weeks | Documents 100% | 7%Half by Nov 2027 |
| Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss measurement, or pricing models.Building the tools and code for portfolio construction, performance and pricing.Done more than monthly by most who do it. AI observed on this task. | 11% | 2.5 work days6.7 h to 1.7 work weeks | Documents 100% | 18%Half by Jun 2027 |
| Collaborate in the development or testing of new analytical software to ensure compliance with user requirements, specifications, or scope.Testing the software against the requirements, with the developers in the loop.Done more than yearly by most who do it. | 8% | 1 work day1.7 h to 4.2 work days | Documents 70%, conversation 30% | 23%Desk part half by Mar 2027 |
| Develop methods of assessing or measuring corporate performance in terms of environmental, social, and governance (ESG) issues.Designing how to score companies on environmental, social and governance issues.Done yearly or less by most who do it. | 5% | 4.2 work days6.7 h to 3.3 work weeks | Documents 100% | 5%Half by Apr 2028 |
| Define or recommend model specifications or data collection methods.Writing down how a model should be built and what data it needs.Done more than yearly by most who do it. | 4% | 6.7 h1.7 h to 2.5 work days | Documents 100% | 55%Half by now |
| Collaborate with product development teams to research, model, validate, or implement quantitative structured solutions for new or expanded markets.Modeling and validating new structured products, in working sessions with the product team.Done more than yearly by most who do it. | 4% | 4.2 work days6.7 h to 3.3 work weeks | Documents 70%, conversation 30% | 4%Desk part half by Apr 2028 |
| Research new financial products or analytics to determine their usefulness.Reading up on new products and methods and judging what they are worth.Done more than yearly by most who do it. | 4% | 2.1 work days3.3 h to 1.7 work weeks | Documents 100% | 15%Half by Oct 2027 |
| Interpret results of financial analysis procedures.Reading what the model outputs mean.Done more than weekly by most who do it. AI observed on this task. | 3% | 1.7 h25 min to 6.7 h | Documents 100% | 88%Half by now |
| Maintain or modify all financial analytic models in use.Changing and fixing the model code in use.Done more than yearly by most who do it. | 3% | 1 work day1.7 h to 4.2 work days | Documents 100% | 33%Half by Mar 2027 |
| Produce written summary reports of financial research results.Writing up the research results.Done more than monthly by most who do it. AI observed on this task. | 2% | 6.7 h1.7 h to 1.7 work days | Documents 100% | 69%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.
Where this job works
The industry groups that employ the most of it (BLS, May 2025). Counts are for the broader BLS group this job sits in.
- Federal Executive Branch27,370 people
- Credit Intermediation and Related Activities (5221 and 5223 only)21,040 people
- Securities, Commodity Contracts, and Other Financial Investments and Related Activities14,410 people
- Management of Companies and Enterprises9,310 people
- Colleges, Universities, and Professional Schools4,610 people
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025). Counts are for the broader BLS group this job sits in.
Questions about this job
- How much of the work of Financial Quantitative Analysts is within reach of AI now?
- An estimated 25% of the working time is within reach of AI models now and 90% by the end of 2028, on METR's long-run pace at four in five (66% to 95% 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 Quantitative Analysts 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: Apply mathematics to finance problems (40% of the task's time now, 99% by the end of 2028); Interpret analysis results (88% of the task's time now, 100% by the end of 2028); and Build independent checks on the systems (15% of the task's time now, 92% by the end of 2028).
- Which tasks of Financial Quantitative Analysts stay with people?
- Every task has a desk part, so each can come partly within reach; the conversation, in-person and hands-on parts of each task stay with people, about 5% of the working time.
- What should Financial Quantitative Analysts 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 4% of the working time stays with people, 89% becomes checking a model's work and 0% 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 Quantitative Analysts?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.21% went to this job's tasks, the 100th largest share of the 447 jobs that showed up; 4 of its 21 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 22%. 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 Quantitative Analysts?
- 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 21 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.
This job in your state or metro area
Embed these figures on your site
A small card with the share of working time within reach now and at the end of 2027, 2028 and 2030, the date and a link back. Free under CC BY 4.0; the card carries the credit.
Your own version of this job
The page above is the occupation. Your job is your title and what you actually do: give both, and they are read against these tasks in your browser, nothing sent anywhere. The headline is free: how much of your working time is within reach now and by the end of 2028, and how many of the tasks your description names.
The full personal report is $19, to print or save as a PDF: every task with when it comes within reach, which ones your description names, the parts of your description the task list does not cover, what stays with you, what becomes checking the machine and what to learn.