Loan Officers
In short: today's best AI models could do about 59% 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 the same by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 41% of the time, stays with people.
Evaluate, authorize, or recommend approval of commercial, real estate, or credit loans. Advise borrowers on financial status and payment methods. Includes mortgage loan officers and agents, collection analysts, loan servicing officers, loan underwriters, and payday loan officers.
274,330 people do this job in the US. $76,690 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on1 h
- A person checks AI's work23 h
- Desk work AI can't do yet0 h
- Stays with people16 h
Loan Officers, now: of a 40-hour week, AI could take on 1 hours, 23 hours become checking its work, 0 hours are desk work it can't do yet, and 16 hours stay with people.
The largest pieces: Assess loan applications (about 6 h, a person checks); Compile applicants' financial records (about 2 h, a person checks); Decide write offs (about 2 h, 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.
- 44%
- 15%
- 23%
- 18%
- 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 59% 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, 1%. With Claude Mythos Preview (early), measured in April 2026, 50%. Today's estimate: 59%.
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, 0.085% went to this job's tasks in May 2026, the 184th largest of the 447 jobs that showed up at all; 3 of its 30 tasks showed up. Within each of Anthropic's methods, never across a change: 0.18% to 0.17% (released February and March 2025); 0.18% to 0.24% (August 2025 to February 2026); 0.079% to 0.085% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.18% | 101 of 708 | 18 of 30 |
| Released March 2025 | 0.17% | 102 of 703 | 12 of 30 |
| The week of Aug 4, 2025 (released September 2025) | 0.18% | 99 of 655 | 12 of 30 |
| The week of Nov 13, 2025 (released January 2026) | 0.16% | 102 of 672 | 13 of 30 |
| The week of Feb 5, 2026 (released March 2026) | 0.24% | 81 of 677 | 15 of 30 |
| April 2026 (released June 2026) | 0.079% | 187 of 434 | 3 of 30 |
| May 2026 (released June 2026) | 0.085% | 184 of 447 | 3 of 30 |
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
41% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Set financial goals with clients, the conversation, 7%
- Meet applicants, the in-person work, 5%
- Explain loan options, the conversation, 5%
- Market bank products, the conversation, 3%
- Supervise loan staff, the conversation, 3%
- Counsel clients on money problems, the in-person work, 3%
What becomes checking the machine
57% 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. Another 2% is desk work that is small if wrong, which can be handed over with lighter checks.
- Assess loan applications, costly if wrong, 15%
- Compile applicants' financial records, costly if wrong, 5%
- Decide write offs, costly if wrong, 4%
- Update loan files, costly if wrong, 4%
- Approve loans within limits, costly if wrong, 3%
- Check loan agreements, costly if wrong, 2%
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 44% 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.
- The conversations. About 41% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Handling protected records. Much of the work touches protected personal records: knowing what may be shared with a model, and how, is part of the job.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
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 . 30 tasks from .
| Task | ||||
|---|---|---|---|---|
| Analyze applicants' financial status, credit, and property evaluations to determine feasibility of granting loans.Analysis of the file.Done daily by most who do it. | 15% | 1.3 h25 min to 2.5 h | Documents 100% | 100%Half by now |
| Work with clients to identify their financial goals and to find ways of reaching those goals.Conversations with the client.Done more than weekly by most who do it. | 7% | 50 min25 min to 1.7 h | Conversation 50%, in person 50% | Stays with people |
| Meet with applicants to obtain information for loan applications and to answer questions about the process.Sitting with the applicant and entering the application.Done daily by most who do it. | 7% | 38 min17 min to 1.3 h | In person 70%, systems 30% | 30%Desk part half by now |
| Obtain and compile copies of loan applicants' credit histories, corporate financial statements, and other financial information.Pulling the reports and assembling the file.Done more than weekly by most who do it. | 5% | 38 min17 min to 1.7 h | Systems 60%, documents 40% | 100%Half by now |
| Explain to customers the different types of loans and credit options that are available, as well as the terms of those services.Talking the options through with the customer.Done daily by most who do it. AI observed on this task. | 5% | 25 min13 min to 50 min | Conversation 50%, in person 50% | Stays with people |
| Stay abreast of new types of loans and other financial services and products to better meet customers' needs.Reading; the knowing is the officer's own.Done more than monthly by most who do it. | 5% | 50 min25 min to 2.5 h | Documents 50%, in person 50% | 50%Desk part half by now |
| Analyze potential loan markets and develop referral networks to locate prospects for loans.Analysis at the desk and calls to prospects and partners.Done more than weekly by most who do it. AI observed on this task. | 5% | 1.7 h50 min to 4.2 h | Documents 50%, conversation 50% | 50%Desk part half by now |
| Supervise loan personnel.Direction and review, mostly in conversation.Done daily by most who do it. | 5% | 50 min25 min to 2.5 h | Conversation 70%, documents 30% | 30%Desk part half by now |
| Review accounts to determine write-offs for collection agencies.Reading the accounts and marking the write offs. | 4% | 1.3 h25 min to 2.5 h | Documents 80%, systems 20% | 100%Half by now |
| Set credit policies, credit lines, procedures and standards in conjunction with senior managers.Policy written, agreed with senior managers.Done more than yearly by most who do it. | 4% | 5 h1.7 h to 2.1 work days | Documents 60%, conversation 40% | 44%Desk part half by now |
| Review and update credit and loan files.Reading and updating the files.Done daily by most who do it. | 4% | 21 min8 min to 42 min | Systems 70%, documents 30% | 100%Half by now |
| Market bank products to individuals and firms, promoting bank services that may meet customers' needs.Calls and visits to customers.Done daily by most who do it. AI observed on this task. | 3% | 50 min25 min to 2.5 h | Conversation 60%, in person 40% | Stays with people |
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.
- Loan Interviewers and Clerks72% now, 72% by 2028
- Credit Counselors73% now, 73% by 2028
- Credit Analysts95% now, 99% by 2028
- Credit Authorizers, Checkers, and Clerks86% now, 86% by 2028
- Personal Financial Advisors71% now, 73% by 2028
- Securities, Commodities, and Financial Services Sales Agents69% now, 73% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Loan Officers is within reach of AI now?
- An estimated 59% of the working time is within reach of AI models now and 59% by the end of 2028, on METR's long-run pace at four in five. 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 Loan Officers 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: Assess loan applications (100% of the task's time now); Compile applicants' financial records (100% of the task's time now); and Decide write offs (100% of the task's time now).
- Which tasks of Loan Officers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Set financial goals with clients; Explain loan options; and Market bank products. Together such tasks are about 25% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Loan Officers 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 the conversations. By the end of 2028 on the long-run pace at four in five, about 41% of the working time stays with people, 57% becomes checking a model's work and 2% 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 Loan Officers?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.085% went to this job's tasks, the 184th largest share of the 447 jobs that showed up; 3 of its 30 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 19%. 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 Loan Officers?
- 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 30 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 September 30, 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.
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