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AI impact reportNo. 345 · revised 5 October 2026 · 250 roles covered

Bank Tellers

Self-service machines, digital onboarding and AI chat are absorbing routine transactions, turning the remaining teller role into service and sales.

Exposure
53
Elevated exposure
higher than 52% of 250 roles
Window
2–6 yrs
until change lands
Adoption today
High
Reading

The role is being reshaped.

Exposure is the share of today's work AI can plausibly take on within the window.

Readers' scoreloading
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We say
53
0┊ our figure 53100

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53

Elevated exposure

little of the workmost of the work
When does change land?
0/600

Bank Tellers

53
01 Overview02 Where you stand03 What this means for you04 Drivers of change05 Impact by sector06 Skills to build07 Tools in use08 In practice09 How this role compares10 Closing judgement11 Evidence and revisions12 Readers' view13 Method and sources
§ 01What is happening

What is happening to bank tellers

Impact

Cash-recycling machines and interactive teller machines handle deposits, withdrawals and cheque processing that used to require a window, mobile apps and digital onboarding open accounts and move money without a visit, and AI assistants in banking apps answer the balance, statement and card questions that once brought customers into the branch. Inside the branch, back-office systems reconcile cash drawers and flag suspicious transactions automatically. What remains for the teller is complex transactions, identity verification, fraud intervention, helping customers who cannot or will not use digital channels, and referring customers to lending and advisory colleagues.

Risk

Elevated exposure: routine transactions keep moving to machines; value shifts to problem-solving, fraud checks and referrals.

The tasks that have automated, and continue to, are cash handling, cheque deposits, transfers, balance enquiries and account opening, all of which now have a self-service or digital path. Tasks that remain human are resolving disputed or unusual transactions, verifying identity in person for high-risk activity, spotting elder-fraud and coercion at the counter, serving customers who need or prefer face-to-face help, and identifying customers who should be referred to a loan officer or adviser. The language-model measures used in the index show very little observed AI usage by tellers themselves, and the score includes an upward editorial adjustment because the real automation channel for this role is physical and self-service, through cash recyclers, kiosks and digital onboarding, which text-usage measures cannot see. Over the 2-6 year window expect continued branch consolidation and fewer teller positions, with those remaining broadened into universal-banker roles that combine transactions, service and sales.

Sector readiness

Mature Self-Service, Shrinking Branch Networks

Banks have invested in self-service and digital channels for well over a decade, and most large institutions now run fewer, smaller branches staffed by universal bankers rather than rows of tellers. AI chat assistants in mobile apps are in production at the big retail banks, and cash-recycling and interactive teller machines are standard in new branch formats. Community banks and credit unions retain more traditional teller lines but are following the same vendor-led path.

§ 02Position

Where you stand

i

Position yourself as a universal banker who can handle transactions, service problems and product referrals in a single conversation.

ii

Be the branch's fraud and identity specialist, trained to spot scams and coercion that automated systems and remote channels miss.

iii

Become the digital coach who helps customers adopt the bank's app and self-service machines, which makes you valuable even as transactions migrate.

§ 03Actions
6 points

What this means for you

Concrete changes to how the work gets done, in the order you are likely to meet them.

  1. 01

    Train as a universal banker. Ask for cross-training in account servicing, basic lending referrals and digital support. Banks are consolidating teller roles into this title and would rather promote than hire.

  2. 02

    Get good at fraud intervention. Learn the signs of elder financial abuse, romance scams and money-mule activity. A teller who stops a fraudulent withdrawal is worth more than one who processes a hundred routine ones.

  3. 03

    Teach the app. Customers who come to the counter because they cannot use digital banking need a patient teacher. Become that person and you turn a cost into a customer-retention asset.

  4. 04

    Learn the referral products. Know enough about mortgages, savings products and small-business accounts to spot a need and hand it to the right colleague. Referral metrics are what branch managers are measured on.

  5. 05

    Collect credentials early. A notary commission, anti-money-laundering certification or a licensing exam for simple investment products moves you up the branch hierarchy and away from the window.

  6. 06

    Watch your branch's footprint. Branch closures are announced well in advance. If your branch is in a consolidating area, start the conversation about transfer or redeployment before the notice arrives.

§ 04Causes
6 drivers

What is pushing this change

  1. 01

    Cash recyclers and interactive teller machines. Machines from NCR Atleos, Diebold Nixdorf and others handle deposits, withdrawals, cheque imaging and coin, with remote video tellers available for anything more complex.

  2. 02

    Mobile and online banking. Most routine transactions and enquiries now happen in the app, so footfall to the branch has fallen and with it the number of transactions per teller.

  3. 03

    Digital onboarding and identity verification. Accounts are opened and identities verified through document scanning and biometric checks on a phone, removing a visit that once brought new customers to the counter.

  4. 04

    AI assistants in banking apps. Conversational assistants handle balance, statement, card and payment questions in the app and contact centre, deflecting the enquiries that remain after transactions moved online.

  5. 05

    Branch consolidation economics. Banks close or shrink branches as transaction volumes fall, and each closure removes teller positions outright rather than converting them.

  6. 06

    Automated cash management and fraud monitoring. Drawer reconciliation, cash ordering and transaction monitoring are automated, cutting back-office teller time and flagging the cases that need human attention.

§ 05Variation
4 sectors

Impact by sector

The headline figure is an average. Where you work changes the picture.

Large national banks

Furthest along in self-service and digital, with heavily reduced branch networks and universal-banker staffing; traditional teller roles are rare in new branch formats.

Community banks

Retain more traditional teller lines and face-to-face relationships, but are adopting cash recyclers and digital onboarding through the same core vendors.

Credit unions

Member-service culture preserves counter staff longer, and many are introducing interactive teller machines and shared-branching rather than closing locations outright.

In-store and supermarket branches

Small-format branches rely on a few multi-skilled staff and self-service machines, making them an early example of the universal-banker model.

§ 06Preparation
6 skills

Skills to build

The skills that keep the human part of this work valuable as the routine part is automated.

  1. 01

    Fraud and scam recognition. Spotting coercion, elder abuse and mule activity at the counter is a judgement task that machines do not perform. Take your bank's fraud training seriously and learn the local scam patterns.

  2. 02

    Needs-based conversation and referral. Turning a transaction into a conversation about a customer's goals is what branch managers want from a teller now. Role-play it and track your referrals.

  3. 03

    Digital banking coaching. Being able to walk a customer through the app, the ITM and online onboarding is a daily need in branches. Use every product yourself so you can teach it.

  4. 04

    Complex transaction handling. Wires, foreign currency, estate accounts and disputed items still land on the counter. Learn the procedures thoroughly so you are the person these are routed to.

  5. 05

    Regulatory and compliance knowledge. Anti-money-laundering, identity verification and reporting rules are the reason some transactions must involve a trained human. Certification here is a direct route to more senior roles.

  6. 06

    Calm service under pressure. Customers come to the branch when something has gone wrong. Handling a distressed or angry customer well is the human core of the job and is noticed by managers.

§ 07Instruments
6 entries

Tools in use

Kinds of tool worth knowing

  1. 01

    Bank-built AI assistants. Large banks run their own in-app assistants for balances, payments and card controls, which deflect the enquiries that once reached the counter.

  2. 02

    Core banking and digital onboarding platforms. Vendors such as Q2 and the major core providers supply the digital account-opening and identity-verification flows that replace in-branch onboarding.

Named tools already in use

  • NCR Atleos self-service and ITM

    Visit

    Cash recyclers, ATMs and interactive teller machines that process branch transactions with optional remote video tellers.

  • Diebold Nixdorf branch automation

    Visit

    Cash-recycling and self-service systems that reduce manual cash handling and reconciliation at the branch.

  • Kasisto KAI

    Visit

    Conversational AI platform used by banks and credit unions for in-app and contact-centre customer assistance.

  • Glia

    Visit

    Digital customer-service platform used by banks and credit unions to blend AI assistance with live chat, video and co-browsing.

§ 08Examples
3 examples

In practice

Ways people in this role are already using AI, and what they get from it.

Cash recycler at the counterExample 1
How

A branch replaces manual drawers with cash recyclers that count, authenticate and dispense notes, so tellers no longer hand-count or reconcile at end of day.

Gain

Balancing errors largely disappear and teller time is freed for customer conversations.

Interactive teller machine in a small-format branchExample 2
How

A credit union installs ITMs that handle standard transactions and connect customers to a remote video teller for anything unusual, staffing the branch with two universal bankers.

Gain

The location stays open with far fewer staff while customers still reach a person when needed.

Scam intervention at the windowExample 3
How

A trained teller notices an anxious customer withdrawing a large sum while on the phone, follows the bank's scam protocol and involves the fraud team before completing the transaction.

Gain

The customer's savings are protected in a situation no self-service channel would have caught.

§ 09Context

How this role compares

Three neighbouring roles chosen to show the direction of travel, then the roles either side of yours on the exposure scale.

CashiersMore exposed · exposure 68
AI impact

Self-checkout and computer-vision stores remove the cashier transaction entirely, with less of the identity and fraud judgement that tellers still provide.

Work moves to

Remaining cashier work shifts to supervising self-checkout and handling exceptions.

Personal Financial AdvisorsDifferent skills, growing · exposure 69
AI impact

AI handles portfolio analytics and planning calculations but the demand for human advice on retirement and major financial decisions is growing.

Work moves to

Tellers who pursue licensing and advisory training have a visible path into a role banks are expanding.

Compliance OfficersComplementary, less exposed · exposure 52
AI impact

AI monitors transactions at scale, but interpreting rules and deciding what to report remains a human responsibility.

Work moves to

Teller experience in identity verification and suspicious-activity reporting is a natural foundation for compliance roles.

Nearby on the scaleExposure · window
  1. Dietitians and Nutritionists

    532–6 yrs
  2. Film and Video Editors

    532–5 yrs
  3. Payroll Managers

    532–5 yrs
  4. Bank Tellers · this report

    532–6 yrs
  5. Animators and Visual Effects Artists

    542–6 yrs
  6. Budget Analysts

    542–6 yrs
  7. Corporate Development Managers

    542–5 yrs

Put this role next to another: vs Cashiers · vs Personal Financial Advisors · vs Compliance Officers · pick any role

§ 10Verdict

Closing judgement

If you work on a teller line, the transaction counting part of your job has been leaving for years and the machines are now better at it than any person. What the branch still needs is someone who can calm a customer whose card was cloned, notice that an elderly customer is being coached through a withdrawal, and know when to introduce a customer to the lending team. Push to be trained as a universal banker, learn the digital products well enough to teach customers to use them, and get the certifications that open the door to lending or advisory roles. The teller title is fading; the people skills behind it are not.

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§ 11Basis
revised 5 October 2026

Evidence and revisions

What the exposure figure rests on, what changed when it was last revised, and the published work cited for this role.

Score

53

Window

2-6 years (unchanged)

The 5 October 2026 review held the score.

Exposure Index v2. Inputs: task applicability 50/100 (Microsoft AI applicability score 0.25 for Tellers); observed usage 3/100 (Anthropic observed exposure 0.02); official exposure tier 70/100 (BLS: high); labour-market trajectory not yet mapped for this occupation, so its weight was spread across the other inputs; published adoption rating 70/100 (high adoption). Weighted base 47.3. Editorial adjustment +6: Self-service banking, cash-recycling machines and digital onboarding have been removing teller transactions for a decade; a physical/self-service channel the text-usage measures cannot see. Final score 53. New report: the window of 2-6 years is set from the score band.

How the figure is builtExposure Index v2
InputScaledWeightPoints
Task applicabilityMicrosoft Research, AI applicability score5039%19.4
Observed usageAnthropic Economic Index, observed exposure322%0.7
Official exposure tierUS BLS AI-exposure category7022%15.6
Labour-market trajectoryUS BLS projected employment change 2025–35not measured——
Published adoption ratingThis report’s adoption level7017%11.7
Weighted base47.3
Editorial adjustment (cap ±12)Self-service banking, cash-recycling machines and digital onboarding have been removing teller transactions for a decade; a physical/self-service channel the text-usage measures cannot see.+6
Exposure score53

Inputs not measured for this occupation are dropped and the other weights renormalised. Scaling rules and the adjustment policy are in the method note below and the research library.

Measures behind the score4 sources

US Bureau of Labor Statistics · Employment Projections 2025–35 and AI Exposure Categories

Official statistics · 27 August 2026

AI-exposure tier: High. Projected employment change not yet mapped for this occupation. Matched to Tellers.

Publisher PDF Archived copy Data

Microsoft Research · Working with AI: Measuring the Applicability of Generative AI to Occupations

Working paper · 10 July 2025

AI applicability score 0.25 for SOC 43-3071; scaled to 50/100 as the task-applicability input.

Anthropic · Anthropic Economic Index report: Cadences

Report · 26 June 2026

Observed exposure 0.02 for SOC 43-3071; scaled to 3/100 as the observed-usage input.

UK Department for Science, Innovation and Technology · Assessment of AI capabilities and the impact on the UK labour market

Report · 28 January 2026

UK context: around 70% of UK workers are in occupations with tasks AI could perform or enhance, above the US average; a one-standard-deviation rise in exposure was associated with a 3.9% fall in UK job postings.

Archived copies are served only where the licence permits; otherwise the link goes to the publisher. Full research library →

§ 12Second opinion

Readers' view

What people who do this work make of our reading: their own scores, their reasons, and the notes they left on each section.

Our report is one reading of the evidence. This section is the other dataset: what people who do or know this work make of it. Nobody has scored this role yet. Sign in to add yours.

Scoresreaders vs. our figure
Readers (mean)

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Readers (median)

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CareerGuard

53

0┊ our figure 53100
Why readers chose their number

No one has explained their score yet. A line or two about what you see in your own work is the most useful thing on this page.

Most helpful notes

No notes yet. Every section above has a “Readers' notes” line at the bottom; open one and say what you know.

§ 13Appendix

Method and sources

Each report was written from a large body of published research. The exposure score itself is computed, not written: it is the CareerGuard Exposure Index, a weighted average of occupation-level measures from the US Bureau of Labor Statistics (AI-exposure classification and 2025–35 projections), Microsoft Research (AI applicability scores) and Anthropic (observed exposure), together with the adoption rating published on the report. The organisations and publications below are the standing literature behind the narrative sections. Every source, with dates, licences and archived copies where we are permitted to hold them, is catalogued in the research library.

Exposure Index v2 (October 2026). Each input is scaled to 0–100 and weighted: task applicability 35% (Microsoft AI applicability score ÷ 0.5), observed usage 20% (Anthropic observed exposure ÷ 0.75), official exposure tier 20% (BLS very high = 100, high = 70, moderate = 40, low = 10), labour-market trajectory 10% (50 − 2.5 × projected % employment change), published adoption rating 15% (very high = 85, high = 70, medium-high = 55, medium = 40, low-medium = 25, low = 10). Inputs not measured for an occupation are dropped and the remaining weights renormalised. An editorial adjustment of at most ±12 points is allowed only for automation channels the measures cannot see (robotics, self-service, machine vision, medical imaging, RPA/OCR, generative video) and is always logged with its reason. Scores are whole numbers, not rounded to five. The change window shifts one notch (a year at each end) per ten points of movement.

Research library: every source, with dates, licences and archived copies →

IGlobal and macroeconomic impact of AI on work
World Economic Forum
The Future of Jobs Report series — Employer survey of expected job growth and decline, skill shifts and technology adoption (2020, 2023 and 2025 editions); Artificial Intelligence and the Future of Entry-Level Work (2026).
AI governance and transformation reports — Frameworks on ethical AI, talent strategy and industry transformation.
McKinsey Global Institute
AI, Automation and the Future of Work series — Research quantifying automation potential by task, sector and demographic, from "Jobs Lost, Jobs Gained" to "Agents, robots, and us" (2025).
Industry-specific reports — Financial services, healthcare, manufacturing and others.
PwC
Global AI Jobs Barometer — Annual analysis of job postings and productivity by AI exposure (2024–2026 editions).
Upskilling Hopes and Fears survey — Employee perceptions and readiness.
Microsoft Research and Anthropic
Working with AI (2025); Anthropic Economic Index (2025–2026) — Occupation-level usage data from Copilot and Claude conversations, the two observed-usage measures behind the 2026 revision.
Stanford Digital Economy Lab and Stanford HAI
Canaries in the Coal Mine? (2025–2026); AI Index Report (annual) — Payroll evidence on early-career employment in exposed occupations; annual measurement of AI capability, investment and adoption.
Deloitte
Human Capital Trends series — Workforce, talent and HR technology trends.
Tech Trends series — Emerging technologies and their business implications.
Accenture
Technology Vision series — Forward-looking analysis of emerging technology, with emphasis on AI.
Fjord Trends — Design, innovation and human experience in a digital world.
Boston Consulting Group
AI/ML insights and industry solutions — "The AI Revolution in the Workplace" and related research.
EY
AI and workforce reports — Adoption, talent strategy and ethics.
IBM Institute for Business Value
AI and automation studies — Business models, workforce evolution and leadership.
OECD
AI Policy Observatory — International data and policy on AI, labour markets and skills.
Employment Outlook — Labour-market trends including technological impact (2023–2026 editions).
International Labour Organization
Generative AI and Jobs: A Refined Global Index of Occupational Exposure (2025) — Task-level exposure gradients for every ISCO occupation; successor to the 2023 global index.
International Monetary Fund
Staff Discussion Notes on AI and work (2024, 2026) — Complementarity framing: where AI augments and where it substitutes.
UK Department for Science, Innovation and Technology
Assessment of AI capabilities and the impact on the UK labour market (2026) — UK occupational exposure and job-posting evidence.
Brookings Institution
AI and automation research — Economic and social implications, displacement and skills.
Yale Budget Lab and Goldman Sachs Research
Tracking the Impact of AI on the Labor Market; AI and the US labour market (2026) — Aggregate labour-market monitoring; macro displacement estimates.
Oxford University (Oxford Martin School)
The Future of Employment — Frey & Osborne and subsequent research on susceptibility to automation.
MIT Technology Review
AI & Work — Reporting on AI research and its implications for industries and jobs.
Gartner
Hype Cycle for Artificial Intelligence — Maturity and adoption of AI technologies.
Future of Work reports — Workplace models and talent strategy.
U.S. Bureau of Labor Statistics
Employment Projections 2025–35; AI Exposure Categories; Occupational Outlook Handbook — Ten-year employment projections and, from the 2025 cycle, an AI-exposure tier for every detailed occupation.
Indeed Hiring Lab
AI at Work Report (2025) and posting-market updates — Skill-level transformation estimates and job-posting trends by occupation.
IICore AI and machine-learning research
OpenAI
Research papers, blog and API documentation — Large language models, generative AI, safety and societal impact.
Google DeepMind
Research papers and blog — Reinforcement learning, AI for science, AGI and ethics.
Meta AI
Research papers and blog — Large language models, computer vision, AI for social good.
Hugging Face
Transformers library and model hub — Open-source state-of-the-art NLP models.
TensorFlow and PyTorch
Documentation and community forums — Core frameworks illustrating practical capability.
arXiv
cs.AI, cs.LG, cs.CV, cs.CL — Pre-print research.
NeurIPS and ICML
Conference proceedings — Top-tier academic research.
ACM and IEEE
Journals and proceedings — ACM Computing Surveys; IEEE Transactions on AI.
Kaggle
Datasets and competition solutions — Applied machine learning on real-world problems.
The Alan Turing Institute
Research and reports — Responsible and applied AI.
IIIEthical and responsible AI deployment
NIST
AI Risk Management Framework — Voluntary framework for managing AI risk.
European Commission
AI Act — Risk-tiered legal framework for AI.
Ethics Guidelines for Trustworthy AI — Principles for responsible development.
Partnership on AI
Research and best practice — Responsible AI development.
AI Now Institute
Annual reports — Social implications: power, inequality, rights.
ACM FAccT
Proceedings — Fairness, accountability and transparency.
Data & Society
Publications — Social implications of data-centric technology.
WIPO
Conversation on IP and AI — Intellectual-property implications of AI.
IEEE Global Initiative on Ethics of A/IS
Ethically Aligned Design — Recommendations for ethical AI design.
Center for AI and Digital Policy
Policy briefs — Accountable AI policy.
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