Insurance Underwriters
In short: today's best AI models could do about 100% 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 0% of the time, stays with people.
Review individual applications for insurance to evaluate degree of risk involved and determine acceptance of applications.
105,420 people do this job in the US. $81,370 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 on0 h
- A person checks AI's work40 h
- Desk work AI can't do yet0 h
- Stays with people0 h
Insurance Underwriters, now: of a 40-hour week, AI could take on 0 hours, 40 hours become checking its work, 0 hours are desk work it can't do yet, and 0 hours stay with people.
The largest pieces: Examine documents for risk (about 16 h, a person checks); Evaluate catastrophe exposure (about 8 h, a person checks); Rate substandard risks (about 6 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.
- 88%
- 12%
- 0%
- 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 100% 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, 74%. Today's estimate: 100%.
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, no reportable share went to this job's tasks in May 2026; 0 of its 7 tasks showed up. Within each of Anthropic's methods, never across a change: 0.015% to 0.015% (released February and March 2025); 0.0078% to 0.0074% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.015% | 481 of 708 | 3 of 7 |
| Released March 2025 | 0.015% | 461 of 703 | 2 of 7 |
| The week of Aug 4, 2025 (released September 2025) | 0.0078% | 485 of 655 | 1 of 7 |
| The week of Nov 13, 2025 (released January 2026) | 0.015% | 428 of 672 | 2 of 7 |
| The week of Feb 5, 2026 (released March 2026) | 0.0074% | 532 of 677 | 3 of 7 |
| April 2026 (released June 2026) | none | n/a | 0 of 7 |
| May 2026 (released June 2026) | none | n/a | 0 of 7 |
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
0% of the working time: the conversation, in-person and hands-on parts of the tasks.
What becomes checking the machine
100% 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.
- Examine documents for risk, costly if wrong, 41%
- Evaluate catastrophe exposure, costly if wrong, 19%
- Rate substandard risks, costly if wrong, 15%
- Write for information and quotes, costly if wrong, 9%
- Check insurance in force, costly if wrong, 8%
- Authorize reinsurance, costly if wrong, 4%
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 88% 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.
- Running AI inside the business systems. About 12% of the time is work inside business systems, which a model can do only once it is connected: knowing the systems well enough to set that up and watch it.
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 . 7 tasks from .
| Task | ||||
|---|---|---|---|---|
| Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property.Health, finances and property read for the degree of risk.Done hourly or more by most who do it. | 41% | 1.3 h25 min to 3.3 h | Documents 100% | 99%Half by now |
| Evaluate possibility of losses due to catastrophe or excessive insurance.Loss possibilities analyzed on paper.Done daily by most who do it. | 19% | 1.3 h25 min to 3.3 h | Documents 100% | 99%Half by now |
| Decrease value of policy when risk is substandard and specify applicable endorsements or apply rating to ensure safe, profitable distribution of risks, using reference materials.Endorsements and ratings applied from the reference materials.Done several times a day by most who do it. | 15% | 1.1 h25 min to 2.1 h | Documents 70%, systems 30% | 100%Half by now |
| Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain company underwriting policies.Letters to representatives and medical personnel.Done several times a day by most who do it. | 9% | 21 min8 min to 50 min | Documents 100% | 100%Half by now |
| Review company records to determine amount of insurance in force on single risk or group of closely related risks.Records looked up and totaled.Done several times a day by most who do it. | 8% | 29 min13 min to 1.3 h | Systems 60%, documents 40% | 100%Half by now |
| Authorize reinsurance of policy when risk is high.The decision worked out and entered, with the reinsurer.Done more than yearly by most who do it. | 4% | 50 min17 min to 2.5 h | Documents 60%, systems 40% | 100%Half by now |
| Decline excessive risks.Applications read and declined in the system.Done more than weekly by most who do it. AI observed on this task. | 4% | 21 min8 min to 50 min | Documents 80%, systems 20% | 100%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).
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 Insurance Underwriters is within reach of AI now?
- An estimated 100% of the working time is within reach of AI models now and 100% 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 Insurance Underwriters 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: Examine documents for risk (99% of the task's time now); Evaluate catastrophe exposure (99% of the task's time now); and Rate substandard risks (100% of the task's time now).
- Which tasks of Insurance Underwriters stay with people?
- Every task has a desk part, so each can come partly within reach; the conversation, in-person and hands-on parts of each task stay with people, about 0% of the working time.
- What should Insurance Underwriters 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 0% of the working time stays with people, 100% 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 Insurance Underwriters?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 6%. 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 Insurance Underwriters?
- 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 7 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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