Occupation · SOC 13-2051.00
Conduct quantitative analyses of information involving investment programs or financial data of public or private institutions, including valuation of businesses.
Also called: Analyst · Financial Analyst · Investment Analyst · Securities Analyst · Credit Products Officer · Equity Research Analyst · Planning Analyst · Portfolio Manager · Real Estate Analyst · Trust Officer · Accounting Analyst · Acquisition Analyst
Job family: Business and Financial Operations Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-13-2051-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
Independent published positions, read together — not a forecast.
93rd-percentile task overlap — yet about 25,100 openings a year (+5.7% projected, BLS), and observed AI use leans 4678% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 94th | 1.4 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 82nd | 0.9 | |
| AI assistant applicability (Microsoft) High | 85th | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.5), and including AI-powered software (γ 0.9). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.2 · 35th percentile among occupations · Moderate
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs. | 7.7% | |
| Analyze financial or operational performance of companies facing financial difficulties to identify or recommend remedies. | 6.5% | |
| Present oral or written reports on general economic trends, individual corporations, and entire industries. | 4.2% | |
| Draw charts and graphs, using computer spreadsheets, to illustrate technical reports. | 2.4% | |
| Employ financial models to develop solutions to financial problems or to assess the financial or capital impact of transactions. | 2.4% | |
| Monitor fundamental economic, industrial, and corporate developments by analyzing information from financial publications and services, investment banking firms, government agencies, trade publications, company sources, or personal interviews. | 2.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +5.7% by 2034 |
| Projected annual openings | 25,100 |
| Employment 2024 → 2034 | 368,500 → 389,600 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 2 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Financial Analysts · 2413 | 62% | Gradient 4 |
| Financial and Investment Advisers · 2412 | 57% | Gradient 3 |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 46.8% working with AI · 45.1% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 24.7% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Inform investment decisions by analyzing financial information to forecast business, industry, or economic conditions. | Directive | 6.4% |
| Interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs. | Learning | 2.5% |
| Recommend investments and investment timing to companies, investment firm staff, or the public. | Directive | 1.7% |
| Draw charts and graphs, using computer spreadsheets, to illustrate technical reports. | Directive | 1.5% |
| Monitor fundamental economic, industrial, and corporate developments by analyzing information from financial publications and services, investment banking firms, government agencies, trade publications, company sources, or personal interviews. | Directive | 1.4% |
| Present oral or written reports on general economic trends, individual corporations, and entire industries. | Directive | 0.6% |
| Evaluate and compare the relative quality of various securities in a given industry. | Directive | 0.6% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Present oral or written reports on general economic trends, individual corporations, and entire industries. | 100.0% | |
| Evaluate and compare the relative quality of various securities in a given industry. | 96.4% | |
| Monitor fundamental economic, industrial, and corporate developments by analyzing information from financial publications and services, investment banking firms, government agencies, trade publications, company sources, or personal interviews. | 96.3% | |
| Interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs. | 94.3% | |
| Recommend investments and investment timing to companies, investment firm staff, or the public. | 94.1% | |
| Inform investment decisions by analyzing financial information to forecast business, industry, or economic conditions. | 92.6% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me inform investment decisions by analyzing financial information to forecast business, industry, or economic conditions. From: Inform investment decisions by analyzing financial information to forecast business, industry, or economic conditions. · 6.4% of measured AI use · directive
Help me interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs. From: Interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs. · 2.5% of measured AI use · learning
Help me recommend investments and investment timing to companies, investment firm staff, or the public. From: Recommend investments and investment timing to companies, investment firm staff, or the public. · 1.7% of measured AI use · directive
Help me draw charts and graphs, using computer spreadsheets, to illustrate technical reports. From: Draw charts and graphs, using computer spreadsheets, to illustrate technical reports. · 1.5% of measured AI use · directive
All 26 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 180.
What to study: Business, Management, Marketing, and Related Support Services , Mathematics and Statistics , Multi/Interdisciplinary Studies . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
The interests and personal qualities O*NET associates with people who do this work.
| Finance | 6.6 | |
| Mathematics/Statistics | 5.3 | |
| Business Initiatives | 5.1 | |
| Accounting | 5.0 | |
| Public Speaking | 3.6 | |
| Management/Administration | 3.5 | |
| Office Work | 3.3 | |
| Sales | 3.3 | |
| Information Technology | 2.7 |
| Dependability | 6.0 | |
| Attention to Detail | 5.0 | |
| Integrity | 4.0 | |
| Cautiousness | 3.0 |
| Conventional | 5.5 | |
| Enterprising | 5.1 | |
| Investigative | 5.1 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $62,410 |
| 25th percentile | $78,300 |
| Median (50th) | $101,350 |
| 75th percentile | $132,050 |
| 90th percentile | $180,550 |
| People employed | 340,580 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Finance and Insurance · Sector | 150,630 | $106,740 |
| Professional, Scientific, and Technical Services · Sector | 49,110 | $100,960 |
| Management of Companies and Enterprises · Sector | 45,100 | $99,760 |
| Manufacturing · Sector | 15,980 | $101,670 |
| Information · Sector | 11,700 | $105,240 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 9,670 | $91,500 |
| Health Care and Social Assistance · Sector | 9,670 | $86,420 |
| Educational Services · Sector | 9,340 | $83,940 |
| Real Estate and Rental and Leasing · Sector | 9,050 | $99,110 |
| Wholesale Trade · Sector | 6,740 | $94,550 |
| Insurance Agencies and Brokerages · National industry | 4,320 | $82,840 |
| Direct Health and Medical Insurance Carriers · National industry | 4,120 | $85,110 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Finance and Insurance · Sector | 10.95× | 150,630 |
| Management of Companies and Enterprises · Sector | 7.27× | 45,100 |
| Solar Electric Power Generation · National industry | 4.22× | 130 |
| Direct Health and Medical Insurance Carriers · National industry | 4.15× | 4,120 |
| Research and Development in the Social Sciences and Humanities · National industry | 2.61× | 350 |
| Professional, Scientific, and Technical Services · Sector | 2.06× | 49,110 |
| Insurance Agencies and Brokerages · National industry | 1.98× | 4,320 |
| Information · Sector | 1.82× | 11,700 |
Part of the Financial Services career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Financial and Investment Analysts — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 97th percentile of 427 international occupations.
Financial and Investment Analysts show 93rd-percentile AI task overlap — and about 25,100 annual U.S. openings
Financial and Investment Analysts show 93rd-percentile AI task overlap — and about 25,100 annual U.S. openings • Financial and Investment Analysts rank in the 93rd percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 25,100 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+5.7%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $101,350, across about 340,580 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 47% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Financial and Investment Analysts". https://singulariki.com/roles/role-13-2051-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
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Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Financial and Investment Analysts." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-13-2051-00
Singulariki. (2026). Financial and Investment Analysts. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-13-2051-00
@misc{singulariki-role-13-2051-00,
title = {Financial and Investment Analysts},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-13-2051-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.