Credit Analysts
In short: today's best AI models could do about 95% 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 99% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 1% of the time, stays with people.
Analyze credit data and financial statements of individuals or firms to determine the degree of risk involved in extending credit or lending money. Prepare reports with credit information for use in decisionmaking.
64,390 people do this job in the US. $83,510 a year.
Reviewed on October 2, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on0 h
- A person checks AI's work38 h
- Desk work AI can't do yet1 h
- Stays with people1 h
Credit Analysts, now: of a 40-hour week, AI could take on 0 hours, 38 hours become checking its work, 1 hours are desk work it can't do yet, and 1 hours stay with people.
The largest pieces: Analyze credit risk (about 9 h, a person checks); Complete loan applications for committee (about 7 h, a person checks); Compare borrowers with their peers (about 7 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.
- 89%
- 10%
- 1%
- 0%
- 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 95% 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, 0%. With Claude Mythos Preview (early), measured in April 2026, 31%. Today's estimate: 95%.
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, 0.011% went to this job's tasks in May 2026, the 363rd largest of the 447 jobs that showed up at all; 1 of its 11 tasks showed up. Within each of Anthropic's methods, never across a change: 0.022% to 0.009% (released February and March 2025); 0.0028% to 0.011% (August 2025 to February 2026); 0.011% to 0.011% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.022% | 435 of 708 | 4 of 11 |
| Released March 2025 | 0.009% | 523 of 703 | 3 of 11 |
| The week of Aug 4, 2025 (released September 2025) | 0.0028% | 591 of 655 | 1 of 11 |
| The week of Nov 13, 2025 (released January 2026) | 0.0072% | 526 of 672 | 2 of 11 |
| The week of Feb 5, 2026 (released March 2026) | 0.011% | 483 of 677 | 2 of 11 |
| April 2026 (released June 2026) | 0.011% | 347 of 434 | 1 of 11 |
| May 2026 (released June 2026) | 0.011% | 363 of 447 | 1 of 11 |
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
1% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Exchange credit information with others, the conversation, 1%
What becomes checking the machine
98% 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.
- Analyze credit risk, costly if wrong, 23%
- Complete loan applications for committee, costly if wrong, 19%
- Compare borrowers with their peers, costly if wrong, 17%
- Analyze expected loan profitability, costly if wrong, 16%
- Write credit risk reports, costly if wrong, 15%
- Generate financial ratios, costly if wrong, 5%
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 89% 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.
- 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.
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 . 11 tasks from .
| Task | ||||
|---|---|---|---|---|
| Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.Analysis of the statements and credit data to judge the risk.Done hourly or more by most who do it. AI observed on this task. | 23% | 2.5 h50 min to 6.7 h | Documents 100% | 96%Half by now |
| Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval.Writing up the analysis and summary and entering the application for the committee.Done daily by most who do it. | 19% | 3.3 h1.3 h to 1 work day | Documents 60%, systems 40% | 95%Half by now |
| Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.Setting the borrower's figures beside those of similar firms in the same industry and place.Done daily by most who do it. | 17% | 3.3 h1.3 h to 5 h | Documents 100% | 100%Half by now |
| Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans.Analysis of the borrower's growth, management and market to judge what the loan will earn.Done daily by most who do it. | 16% | 2.5 h50 min to 6.7 h | Documents 100% | 96%Half by now |
| Prepare reports that include the degree of risk involved in extending credit or lending money.Writing the credit memo with the risk rating.Done daily by most who do it. | 15% | 2.5 h50 min to 6.7 h | Documents 100% | 96%Half by now |
| Generate financial ratios, using computer programs, to evaluate customers' financial status.Spreading the statements in the program and reading the ratios it gives.Done daily by most who do it. | 5% | 38 min13 min to 1.7 h | Documents 70%, systems 30% | 100%Half by now |
| Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.Reading the customer's record and working out a plan they can pay.Done daily by most who do it. | 2% | 1.3 h25 min to 2.5 h | Documents 100% | 100%Half by now |
| Confer with credit association and other business representatives to exchange credit information.Calls with credit bureaus and other lenders about a customer's record.Done more than monthly by most who do it. | 1% | 50 min25 min to 1.7 h | Conversation 100% | Stays with people |
| Review individual or commercial customer files to identify and select delinquent accounts for collection.Working through the account files in the system to pick which go to collection.Done more than yearly by most who do it. | 0.5% | 50 min25 min to 2.5 h | Documents 50%, systems 50% | 100%Half by now |
| Consult with customers to resolve complaints and verify financial and credit transactions.Calls with customers about complaints and transactions, checking the account as they talk.Done more than monthly by most who do it. | 0.3% | 38 min13 min to 1.3 h | Conversation 70%, systems 30% | 30%Desk part half by now |
| Contact customers to collect payments on delinquent accounts.Calls to customers behind on payments, with the account noted as they go.Done more than monthly by most who do it. | 0.0% | 21 min8 min to 50 min | Conversation 70%, systems 30% | 30%Desk part half by now |
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.
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
- Credit Intermediation and Related Activities (5221 and 5223 only)30,150 people
- Nondepository Credit Intermediation10,220 people
- Management of Companies and Enterprises7,830 people
- Securities, Commodity Contracts, and Other Financial Investments and Related Activities5,330 people
- Merchant Wholesalers, Durable Goods (4232, 4233, 4235, 4236, 4237, and 4239 only)1,310 people
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 Credit Analysts is within reach of AI now?
- An estimated 95% of the working time is within reach of AI models now and 99% 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 Credit Analysts 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: Analyze credit risk (96% of the task's time now); Complete loan applications for committee (95% of the task's time now, 100% by the end of 2028); and Compare borrowers with their peers (100% of the task's time now).
- Which tasks of Credit Analysts stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Exchange credit information with others. Together such tasks are about 1% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Credit Analysts 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 handling protected records. By the end of 2028 on the long-run pace at four in five, about 1% of the working time stays with people, 98% becomes checking a model's work and 0% 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 Credit Analysts?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.011% went to this job's tasks, the 363rd largest share of the 447 jobs that showed up; 1 of its 11 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 17%. 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 Credit Analysts?
- 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 11 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 October 2, 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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