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Will AI replace new accounts clerks?

A little.

Opening an account turns on identity judgment, product explanation and a named person who owns the decision, so software mostly assists. This job scores 66 out of 100 on (higher is safer). Today people do 73% of the work with AI’s help, and 27% still needs a person.

Updated 3 October 2026 43-4141 4123 2026-Q4
Office and Administrative SupportNew Accounts Clerks43-4141 · 2026-Q4
0% AI does it73% AI helps27% needs a human
Your job's name, lit by the work that still needs a human.Needs a human 27%AI helps 73%AI does it 0%

AI does it: AI can do the task largely by itself. AI helps: a person still does it, faster with AI. Needs a human: AI can do little of it yet.

Will AI replace new accounts clerks? The scores above give the short answer, and the reason sits in the task mix. Most of the paperwork around opening an account is already digital, so software can read documents, check records and fill forms. What it cannot do on its own is sit with a worried customer, judge a thin or odd identity file, and take responsibility for the account that gets opened.

This page explains the figures above in plain words: what automation handles, what it only assists with, how strong the evidence is, and when the balance could shift. You can read how each score is built on our scoring method page.

Why account opening keeps a person at the desk

New accounts clerks do two jobs at once. One is clerical: collect information, verify identification, complete the application, set up checks and cards, and file the record. The other is advisory: explain the difference between account types, answer questions about fees and limits, calm someone whose direct deposit has not arrived, and spot the application that does not add up.

The clerical half moves to software easily, because banks already run account opening through online forms and automated identity and fraud screening. The advisory half moves slowly. Explaining options to a customer who is not sure what they need takes back-and-forth listening. Verifying identity when the documents are incomplete, mismatched or unusual takes a judgment call that someone has to own, with a regulator looking over the bank’s shoulder.

There is also a physical share to the work. Documents get handled, signatures get witnessed, cards and checkbooks get issued at a counter. The robotics panel above puts that work in reach of mobile machines in principle, but branch service is not where banks are spending on hardware. The practical pressure on this job is software and self-service, not robots.

What software does, what it assists with, and what people keep

Document and data work is the part automation handles without much help. Reading an ID, pulling a credit file, matching details against records and populating an application form are repeatable steps with a clear right answer. Across this job’s tasks, the share marked as work AI can do is 0%. That is the share of task time, not a share of jobs.

A bigger slice is assisted rather than taken. Here, software drafts and the clerk decides: suggested account options to review, pre-filled disclosures to confirm, fraud flags to clear or escalate, follow-up messages to approve. The assisted share is 73%. Coverage overall reads 38 out of 100, and our Can AI do it? method explains what that measures.

The rest stays with people: 27% of task time. That is the customer conversation that changes direction halfway through, the exception case that needs a named person to sign off, the complaint that has to be resolved rather than routed, and the referral to a loan officer or branch manager who can actually act.

How solid is the evidence on this job

Not very, and the page says so. The evidence grade for how well AI performs against a qualified person in this role is D. A D grade means no direct test against people doing this work has been published, so no parity number is given here. We would rather leave it blank than guess.

What would settle it is straightforward: a measured comparison of account-opening accuracy and completion between staffed desks and automated onboarding, including how often exceptions need a human, plus published error and fraud rates on identity decisions. Until that exists, the honest reading is uncertainty.

Official labor data is firmer. BLS counts about 36,860 people in this occupation with median pay of $47,670 (BLS, 2025), and its projections point to a decline of roughly 6.5% between 2025 and 2035. That pattern matches the story across bank back offices: fewer new seats, more work per person, rather than a sudden exit. Our guide on entry-level hiring covers why that squeeze lands hardest on new starters.

When the balance could shift

Most likely between 2036 and 2050 (8 in 10 of our scenarios). The replacement-year method sets out exactly what that window covers and how the range is produced.

Two things could pull it earlier. First, cost: the comparison above shows a wide gap between running software and staffing a desk, and that gap is what funds self-service onboarding. Second, channel shift. Every account opened in an app instead of a branch removes a clerk’s task before any model has to be clever.

Two things hold it back. Identity verification and anti-money-laundering rules put accountability on named staff, and banks are slow to hand a compliance decision to a system with no clear audit trail. And exceptions are stubborn: the customer with no fixed address, the trust account, the business owner with mismatched records. Those cases are a small share of volume and a large share of the work.

Good to know: the risk here shows up first as fewer openings for new clerks, not as current staff walking out the door.

How to stay needed in a new accounts role

Lean into the parts of the job that carry responsibility. Three worth building on: handling exception and high-risk account cases rather than clean applications; resolving customer problems end to end instead of passing them on; and referring customers to the right product or specialist, which is where branches still make money.

Two skills travel well. One is compliance literacy: knowing the identity, fraud and reporting rules well enough to defend a decision. The other is reviewing machine output, so you can tell a correct automated flag from a false one and explain it to a customer in a sentence.

Nearby jobs share a lot of the same ground, so they are worth a look if you are planning a move. Closest in work are Loan Interviewers and Clerks, then Tellers and Credit Authorizers, Checkers, and Clerks. You can see the whole group on the information and record clerks family page, and the wider picture on the banking sector page.

The headline score for this job reads 66 out of 100 (higher is safer). To see how that sits against the roles you are considering, put two of them side by side in the job comparison tool, or check where clerical work sits on our list of jobs most at risk.

Frequently asked questions

Will accounts payable clerks be replaced by AI?

Accounts payable is one of the most automated clerical areas, because invoice capture, coding and matching follow rules with clear answers. The work that remains is exception handling, vendor disputes, approvals and fraud checks. The pattern looks like a smaller team doing more volume rather than the function disappearing. Each clerical job has its own page here, so compare the task splits side by side before drawing conclusions.

Are CPAs in danger of AI?

Licensed accountants sit further from automation than clerks, because their work involves judgment, sign-off and legal responsibility. Software now drafts reconciliations, flags anomalies and prepares returns for review. That shifts the day toward advisory and oversight work and away from preparation. The risk for CPAs is less about the license and more about how many junior preparation roles firms still need to hire.

Will staff accountants be replaced by AI?

Staff accountant roles are exposed on the preparation side: journal entries, reconciliations, schedules and month-end cleanup. Review, explanation and audit defense stay with people. The honest concern is entry points. If software handles the first two years of tasks, firms hire fewer juniors, and the ladder into senior work gets narrower. The task list on each accounting job page shows where that line falls.

Will AI replace bank tellers before new accounts clerks?

Teller work is more transactional, and much of it already moved to ATMs, deposit apps and self-service. Account opening involves more explanation and more identity judgment, which is slower to automate. Rather than rank them from memory, open both job pages here and read the task splits and evidence grades; the figures update with each release, so the pages are the reliable version.

What skills should a new accounts clerk build now?

Three are worth the effort. Compliance knowledge, so you can make and explain identity and anti-money-laundering decisions. Customer problem solving, so complaints end with you instead of a transfer. And comfort reviewing automated output, including fraud flags and pre-filled applications, so you catch errors quickly. Product knowledge also helps, because referrals to lending and advisory staff still need a person who understands the customer.

Is new accounts clerk still a good career path?

It is still a real entry point into banking, and BLS reports median pay of $47,670 for the occupation (BLS, 2025). But its projections point to shrinking employment through 2035, so treat the role as a step rather than a destination. Clerks who learn compliance, lending basics or branch operations tend to move into loan, fraud or supervisory work.

Each ridge is a slice of the job's task time.Needs a human 27%AI helps 73%AI does it 0%
The job’s mark

No two jobs leave the same print

Every job gets its own fingerprint, drawn from its code. The amber ridges are the share of task time that still needs a person. Below them, the same ridges are written out in ones and zeros: slate for the work AI helps with, white for the work AI can do.

New Accounts Clerks, O*NET-SOC 43-4141. 27% of the job’s task time still needs a human, so 27 of every 100 ridges are amber; slate is what AI helps with, white what AI can do.

What AI can and cannot do

The tasks that make up the job, from , and where AI stands on each today: , (a person does it, with AI speeding it up) or . 27% of the still needs a human.

Each block is one task; its height is its share of working time.Needs a human 27%AI helps 73%AI does it 0%
The job's task list: the parts AI can do are blacked out.Needs a human 27%AI helps 73%AI does it 0%
Perform teller duties as required.Needs a human
Compile information about new accounts, enter account information into computers, and file related forms or other documents.AI helps
Collect and record customer deposits and fees and issue receipts, using computers.Needs a human
Inform customers of procedures for applying for services, such as ATM cards, direct deposit of checks, and certificates of deposit.AI helps
Answer customers' questions and explain available services, such as deposit accounts, bonds, and securities.AI helps
Interview customers to obtain information needed for opening accounts or renting safe-deposit boxes.AI helps
Refer customers to appropriate bank personnel to meet their financial needs.AI helps
Investigate and correct errors upon customers' request, according to customer and bank records.AI helps
Execute wire transfers of funds.AI helps
Issue initial and replacement safe-deposit keys to customers, and admit customers to vaults.Needs a human
Process loan applications.AI helps
Obtain credit records from reporting agencies.AI helps
Schedule repairs for locks on safe-deposit boxes.AI helps
Perform foreign currency transactions and sell traveler's checks.Needs a human
Duplicate records for distribution to branch offices.AI helps

Is it better than a person? The evidence

No direct test against people in this job yet. Every study is , and vendor studies are labelled as such.

When could it be replaced?

When AI could largely do this job: 2036–2050

Most likely between 2036 and 2050 (8 in 10 of our scenarios). A range from our of how fast AI improves, how fast employers take it up and what holds it back, not a forecast that the job ends. “” has a strict meaning here. Today’s answer is at the top of the page; this is how it could change.

The sand is the human working years left, measured in the same 40-year glass for every job, so a safe trade starts nearly full and an exposed job with a thin layer.

The sand is the human working years left, in the same 40-year glass for every job.Years still needing a humanYears run out

How this job could shift, year by year

Where the job could sit on our scale each year to 2060, across the ten behind its .

Today
Will AI replace this job?
A little.
By 2045
90%
of our scenarios have AI largely doing this job by 2045 (Largely.)
0% still have it mostly needing a person (A little. or Nah.)
By 2060
100%
of our scenarios have AI largely doing this job by 2060 (Largely.)
0% still have it mostly needing a person (A little. or Nah.)

We run this job as ten scenarios spread across its replacement range. In each, the score moves towards the bottom band (Largely: AI could largely do the job) by the year that scenario reaches it, slowly at first and faster later, as adoption usually goes. Each bar splits the ten by the band they put the job in. The model stops at 2060. How the timeline works

Share of this job's scenarios in each verdict band, today to 20600%25%50%75%100%2026: 100.0% of scenarios: AI could do a little of this job (A little.)100%Today2030: 30.0% of scenarios: AI could do a little of this job (A little.)30%2030: 70.0% of scenarios: AI could partly do this job (Partly.)70%20302035: 40.0% of scenarios: AI could partly do this job (Partly.)40%2035: 40.0% of scenarios: AI could mostly do this job (Mostly.)40%2035: 20.0% of scenarios: AI could largely do this job (Largely.)20%20352040: 40.0% of scenarios: AI could mostly do this job (Mostly.)40%2040: 60.0% of scenarios: AI could largely do this job (Largely.)60%20402045: 10.0% of scenarios: AI could mostly do this job (Mostly.)10%2045: 90.0% of scenarios: AI could largely do this job (Largely.)90%20452050: 100.0% of scenarios: AI could largely do this job (Largely.)100%20502055: 100.0% of scenarios: AI could largely do this job (Largely.)100%20552060: 100.0% of scenarios: AI could largely do this job (Largely.)100%2060
Will AI replace the job?Largely.Mostly.Partly.A little.Nah.
Share of this job's scenarios in each band, year by year. Updated with every release.
Show the data
YearLargelyMostlyPartlyA littleNah
Today (2026)0.0%0.0%0.0%100.0%0.0%
20300.0%0.0%70.0%30.0%0.0%
203520.0%40.0%40.0%0.0%0.0%
204060.0%40.0%0.0%0.0%0.0%
204590.0%10.0%0.0%0.0%0.0%
2050100.0%0.0%0.0%0.0%0.0%
2055100.0%0.0%0.0%0.0%0.0%
2060100.0%0.0%0.0%0.0%0.0%

What’s stopping AI taking over?

The things that keep this work with people, strongest first. Each is scored 0 to 100 from work context, licensing and the evidence we have.

Clients want a personFace-to-face contact is rated 4.7 and physical closeness 3.9 out of 5; caring for or serving people is 3.1 out of 5 in importance.
LiabilityMistakes are rated 3.1 out of 5 for consequence and decisions 3.9 out of 5 for impact; someone has to answer for them.
Evidence gapNo study yet compares AI with people doing this job, so employers have no proof it is good enough.
RegulationWorkers rate responsibility for others' health and safety 2.4 out of 5.
LicensingUsual entry requirement (BLS): high school diploma or equivalent, then moderate-term on-the-job training; 1 task statement mentions a licence or certification.
Physical work27% of the task time is physical; robots have been shown on 100% of that time.

What would it cost to hand the work to AI?

The share of the year AI could handle (795 of 2,080 hours a year), priced two ways. Both are ranges, not quotes.

AI model usage, a year
$80–$7,950
A person’s wage for the same hours
$14,580–$23,270

AI cost covers model usage only: no integration, licences, oversight or the human time still needed to review the work. Human cost is the wage for the same hours, without benefits or overheads. As of 2026-10.

Robots and humanoids

AI software can only take the work at a screen. The rest needs a robot that can do it.

27%
of the task time is physical work
Mobile robots
the kind of robot the physical work would need
Commercial in warehouses, hospitals and some outdoor sites; hands are still limited.

Source: Anthropic Economic Index, 'What work can robots do?' (30 September 2026); O*NET 31.0 task weights.

Which AI skills does this job lean on?

The job’s task time split by what an AI model would need to be good at, and where models stand today.

Each star is a task, grouped by the AI skill it leans on.Needs a human 27%AI helps 73%AI does it 0%
Writing · 0% of time
Strong
Drafts, edits and translates most routine documents at professional quality.
Analysis · 5.8% of time
Strong
Reliable on structured data and rules; uneven on judgement calls with thin information.
Coding · 0% of time
Strong
Agents complete many routine software tasks end to end; larger systems still need people.
Vision and design · 0% of time
Good
Reads documents, images and layouts well; specialist imaging needs dedicated, approved tools.
Speech · 25.1% of time
Good
Voice agents handle routine calls and live interpreting; complex or sensitive calls still go to people.
Planning and agents · 50.8% of time
Emerging
Multi-step agents work in narrow, well-tooled workflows; open-ended coordination is unreliable.
Physical manipulation · 18.3% of time
Early
Robots handle structured, repetitive handling; general dexterity outside fixed settings is not commercial.
Care and persuasion · 0% of time
Limited
Can script, coach and advise; trust, presence and accountability still need a person.
Amber matter holds its orbit, slate circles the inner disc, white falls in.Needs a human 27%AI helps 73%AI does it 0%
How exposed is it?

Still needs a human: 66/100↑ safer

The amber matter on the outside holds its orbit: that is the task time that still needs a person. The slate matter circling the inner disc is the work AI helps with. The white matter is the share AI can do; it spirals in and is gone over the edge.

The matter follows the job’s task split: 27% needs a human, 73% AI helps, 0% AI does it. Still needs a human: 66/100 ↑ safer. Will AI replace them? A little.

What the AIs say

We asked four AI assistants the same question: will AI replace this job in the next 10 years? One word (Yes, Partly or No), then one sentence. Our own answer, from the data: Still needs a human: 66/100 ↑ safer. Will AI replace them? A little.

ChatGPTPartly

AI will automate many routine bookkeeping, data entry, and reconciliation tasks, but human clerks will still be needed for exceptions, judgment, compliance, and communication.

gpt-5.5-2026-04-23 · asked 2026-10-03
ClaudeYes

Much of the new accounts clerk role involves routine data entry, verification, and document processing that AI and automation can handle efficiently, though some human oversight will likely remain for complex cases and customer interactions.

claude-sonnet-5 · asked 2026-10-03
GeminiPartly

While AI will automate routine data entry and verification tasks, human clerks will still be needed to handle complex customer inquiries, resolve discrepancies, and build client relationships.

gemini-3.8-flash · asked 2026-10-03
PerplexityPartly

AI will likely automate many new-accounts-clerk tasks and reduce hiring, but human handling of exceptions, compliance, and customer judgment will remain necessary.

sonar · asked 2026-10-03

Assistants answer from what they learned in training (Perplexity also searches the web), so they can be confidently wrong, and the same question can get a different answer tomorrow. Our score is built from task data and graded evidence. Answers collected through DataForSEO.

Cite this page

NeedsAHuman.com (2026). Will AI replace New Accounts Clerks? A little. Still needs a human: 66/100, higher is safer; release 2026-Q4. https://needsahuman.com/jobs/new-accounts-clerks/ (accessed 4 October 2026).

Scores change with each , so cite the release. The data is open under : credit NeedsAHuman.com with a link. Open data · Press

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The badge updates itself with each release and links back to this page.

Sources

  • Tasks and work context: 31.0, ().
  • Jobs, pay and projections: US , and 2025–35.
  • How AI is used today: ; Microsoft Research, .
  • What AI can do: our task ratings ( r1) and the quality evidence register.
  • UK names and employment: coding index and .

How each score is built: methodology. Every figure on this page: open data. Release 2026-Q4.