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Singulariki

Tellers

Occupation · SOC 43-3071.00

Receive and pay out money. Keep records of money and negotiable instruments involved in a financial institution's various transactions.

Also called: Bank Teller · Financial Services Representative (FSR) · Member Services Representative · Teller · Account Representative · Branch Operations Specialist · Customer Relationship Specialist · Customer Service Associate (CSA) · Personal Banking Representative · Roving Teller · Bank Representative · Banker

Job family: Office and Administrative Support Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-43-3071-00/context.md directly.

AI work map

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.

Often handed to AI

Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.

  • Compute financial fees, interest, and service charges. · 0.4%
See how AI is used here →

Keep a human in the loop

Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.

  • Compute financial fees, interest, and service charges. · 100.0% need a human
See the boundary tasks →

67th-percentile task overlap — yet about 29,800 openings a year (-12.9% projected, BLS) . What exposure means →

AI & job outlook

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.

Exposure to current AI

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.) Moderate 59th 0.5
LLM task exposure, γ (OpenAI / Eloundou) Moderate 61st 0.8
AI assistant applicability (Microsoft) High 80th 0.2

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.3), with simple added tooling (β 0.5), and including AI-powered software (γ 0.8). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Historical automation estimate (2013)

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 1.0 · 97th percentile among occupations · High

How AI is actually used in this job

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.

Identify transaction mistakes when debits and credits do not balance. 0.6%
Compute financial fees, interest, and service charges. 0.6%

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook Declining · -12.9% by 2034
Projected annual openings 29,800
Employment 2024 → 2034 347,400 → 302,500

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

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 occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

58% mean task exposure (2025)
96th percentile of 427 placed occupations
−14 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Bank Tellers and Related Clerks · 4211 58% 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.

Working with AI in this job

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.

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) 60.0%

What people delegate to AI

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
Compute financial fees, interest, and service charges. Directive 0.4%

Where a human is still needed

Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.

Compute financial fees, interest, and service charges. 100.0%

What people most often hand AI here

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 compute financial fees, interest, and service charges.

    From: Compute financial fees, interest, and service charges. · 0.4% of measured AI use · directive

Tasks

All 29 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Abilities

Oral Comprehension 4.0
Oral Expression 3.9
Number Facility 3.9
Speech Recognition 3.9
Speech Clarity 3.8
Problem Sensitivity 3.6
Information Ordering 3.6
Near Vision 3.5
Written Comprehension 3.3
Written Expression 3.3
Selective Attention 3.3
Deductive Reasoning 3.1
Mathematical Reasoning 3.1
Inductive Reasoning 3.0
Category Flexibility 3.0
Perceptual Speed 3.0
Fluency of Ideas 2.9
Flexibility of Closure 2.9
Time Sharing 2.9

Knowledge

Customer and Personal Service 3.9
English Language 3.6
Mathematics 3.5
Economics and Accounting 3.1
Public Safety and Security 3.0
Administration and Management 2.9

Essential skills

Active Listening 3.5
Speaking 3.3
Reading Comprehension 3.1
Critical Thinking 3.1
Monitoring 3.1
Writing 3.0
Mathematics 3.0
Active Learning 2.9

Transferable skills

Social Perceptiveness 3.1
Service Orientation 3.1
Time Management 3.0
Coordination 2.9
Instructing 2.9
Complex Problem Solving 2.9
Judgment and Decision Making 2.9

Skills in demand

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 41.

Tools & technology

Example Category
Microsoft Office software Office suite software Hot technology In demand
Microsoft Excel Spreadsheet software Hot technology
Microsoft Outlook Electronic mail software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Microsoft Windows Operating system software Hot technology
Microsoft Word Word processing software Hot technology
Email software Electronic mail software
Hyland Software OnBase Document management software
IBM Notes Electronic mail software
Information Technology Incorporated Premier Teller Accounting software
Jack Henry & Associates Vertex Enterprise resource planning ERP software
Microsoft Dynamics Enterprise resource planning ERP software
Microsoft Exchange Electronic mail software
Sage 50 Accounting Accounting software
Southern Data Systems TellerPro Accounting software
Total Turnkey Solutions E-Vision Data base user interface and query software

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

Contact With Others 5.0
Telephone Conversations 4.9
Face-to-Face Discussions with Individuals and Within Teams 4.9
E-Mail 4.8
Importance of Being Exact or Accurate 4.8
Importance of Repeating Same Tasks 4.7
Indoors, Environmentally Controlled 4.7
Spend Time Making Repetitive Motions 4.6
Work With or Contribute to a Work Group or Team 4.5
Frequency of Decision Making 4.3
Impact of Decisions on Co-workers or Company Results 4.2
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.1
Deal With External Customers or the Public in General 4.1
Physical Proximity 4.0
Dealing With Unpleasant, Angry, or Discourteous People 3.9
Coordinate or Lead Others in Accomplishing Work Activities 3.8
Determine Tasks, Priorities and Goals 3.8
Freedom to Make Decisions 3.7
Health and Safety of Other Workers 3.6
Spend Time Standing 3.5
Time Pressure 3.5
Written Letters and Memos 3.4
Conflict Situations 3.4
Level of Competition 3.3
Work Outcomes and Results of Other Workers 3.2
Consequence of Error 3.2
Spend Time Sitting 3.1
Degree of Automation 2.5
Public Speaking 2.4
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 2.4
Exposed to Disease or Infections 2.4
Dealing with Violent or Physically Aggressive People 2.3
Spend Time Walking or Running 2.3
Spend Time Bending or Twisting Your Body 2.2
Pace Determined by Speed of Equipment 1.9
Exposed to Contaminants 1.8
Exposed to Extremely Bright or Inadequate Lighting Conditions 1.7
Exposed to Cramped Work Space, Awkward Positions 1.6
Spend Time Keeping or Regaining Balance 1.5
Spend Time Kneeling, Crouching, Stooping, or Crawling 1.5

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

What to study: Business, Management, Marketing, and Related Support Services . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

High School Diploma 73.0%
Some College Courses 9.0%
Bachelor's Degree 8.3%
Associate's Degree (or other 2-year degree) 5.4%
Post-Secondary Certificate 2.7%
Less than a High School Diploma 1.7%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Conventional 6.9
Enterprising 4.2
Social 2.4
Realistic 2.3

Interest areas

Office Work 5.8
Accounting 5.3
Finance 3.3
Sales 2.5
Personal Service 2.4
Mathematics/Statistics 1.7

Work styles

Dependability 4.0
Attention to Detail 3.0
Integrity 2.7
Cautiousness 2.5
Cooperation 1.9
Social Orientation 1.7

Wages & employment

U.S. · annual wages (BLS OEWS)

$31k10th$36k25th$39kMedian$46k75th$48k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
347k2024303k2034 (proj.)-12.9% · Declining
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $31,270
25th percentile $36,420
Median (50th) $39,340
75th percentile $45,550
90th percentile $48,270
People employed 339,340

Industries that employ this occupation

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 330,260 $39,280
Management of Companies and Enterprises · Sector 5,170 $44,110
Administrative and Support and Waste Management and Remediation Services · Sector 2,830 $39,600
Temporary Help Services · National industry 340 $34,600
Information · Sector 240 $45,800
Professional, Scientific, and Technical Services · Sector 230 $46,180
Insurance Agencies and Brokerages · National industry 140 $37,260
Arts, Entertainment, and Recreation · Sector 90 $42,490
Educational Services · Sector 80 $38,310
Health Care and Social Assistance · Sector 50 $39,270
Utilities · Sector $50,470
Other Services (except Public Administration) · Sector $33,100

Where this work is most concentrated

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 24.1× 330,260
Management of Companies and Enterprises · Sector 0.84× 5,170
Administrative and Support and Waste Management and Remediation Services · Sector 0.14× 2,830
Insurance Agencies and Brokerages · National industry 0.06× 140
Temporary Help Services · National industry 0.06× 340
Information · Sector 0.04× 240
Professional, Scientific, and Technical Services · Sector 0.01× 230

Part of the Financial Services career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Tellers sits at the 67th percentile of AI task-overlap and the 12th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Tellers Gambling Change Persons and Booth Cashiers Billing and Posting Clerks Credit Analysts Bookkeeping, Accounting, and Auditing Clerks Bill and Account Collectors Securities, Commodities, and Financial Services Sales Agents Customer Service Representatives Brokerage Clerks AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Tellers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 96th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Tellers show 67th-percentile AI task overlap — and about 29,800 annual U.S. openings

  • Tellers rank in the 67th 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 29,800 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 declining (-12.9%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $39,340, across about 339,340 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Tellers show 67th-percentile AI task overlap — and about 29,800 annual U.S. openings

• Tellers rank in the 67th 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 29,800 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 declining (-12.9%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $39,340, across about 339,340 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Tellers". https://singulariki.com/roles/role-43-3071-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

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.

Sources for this page

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.

Cite this page
Plain

Singulariki. "Tellers." 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-43-3071-00

APA

Singulariki. (2026). Tellers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-43-3071-00

BibTeX
@misc{singulariki-role-43-3071-00,
  title  = {Tellers},
  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-43-3071-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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