US dataswitch to UK
Insurance Claims and Policy Processing Clerks
preparing insurance claim forms or related documents, processing and recording new insurance policies and claims and organizing or working with detailed office or warehouse records. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: posting or attaching information to claim file. The tasks, though, are not you.
It would be a lie to soften that, and another lie to promise a rebuilt version of this job inside this job.
So the hope here is what you already carry: the judgment you bring to information is real, and the moves below are built from it. The first step is down this page.
Your week, as this page understands it
Process new insurance policies, modifications to existing policies, and claims forms. Obtain information from policyholders to verify the accuracy and completeness of information on claims forms, applications and related documents, and company records. Update existing policies and company records to reflect changes requested by policyholders and insurance company representatives. The job title says “insurance claims” or “policy processing clerks”: officially one job, two names. The real job is the part underneath: comparing information from application to criteria for policy reinstatement. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But insurance claims and policy processing clerks is not one task. It is 25 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is comparing information from application to criteria for policy reinstatement, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 94%
- changing shape
- 6%
- staying human
- 0%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 79 out of 100 (75–83 allowing for uncertainty): high exposure, across 25 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for insurance claims and policy processing clerks is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
23 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Corresponding with insured or agent to obtain information or to inform them of account status or changes
This is reading one thing and writing another: insured in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Correspond with insured or agent to obtain information or to inform them of account status or changes.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing to customers or agents about account status is routine correspondence that software drafts well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Contacting insured or other involved persons to obtain missing information
This is reading one thing and writing another: insured in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Contact insured or other involved persons to obtain missing information.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Automated reminders chase most missing details, though awkward gaps still need someone to call.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Posting or attaching information to claim file
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Post or attach information to claim file.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Attaching information to the right claim file is a filing step that systems handle automatically.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Organizing or working with detailed office or warehouse records
This is reading one thing and writing another: detailed office in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Organize or work with detailed office or warehouse records, using computers to enter, access, search or retrieve data.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Entering, searching and retrieving detailed records is what computer systems are built to do.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Transmiting claims for payment or further investigation
This is reading one thing and writing another: claims in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Transmit claims for payment or further investigation.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Routing a claim onward for payment or investigation follows fixed rules that software applies.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing insurance claim forms or related documents
This is reading one thing and writing another: insurance claim forms in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Prepare insurance claim forms or related documents, and review them for completeness.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Filling in claim forms and checking them for missing pieces is structured document work software already does.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Processing and recording new insurance policies and claims
This is reading one thing and writing another: new insurance policies in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Process and record new insurance policies and claims.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Setting up new policies and claims in the system is structured data handling that software does quickly.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing insurance policy to determine coverage
This is reading one thing and writing another: insurance policy in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Review insurance policy to determine coverage.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading a policy to see what is covered is close document work, with unusual cases still checked by a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
2 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Comparing information from application to criteria for policy reinstatement
The software now makes the first pass at information, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Compare information from application to criteria for policy reinstatement, and approve reinstatement when criteria are met.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Matching an application to reinstatement rules is easy; actually approving it puts the firm on the hook, so a person decides.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Interviewing clients and taking their calls to provide customer service and obtain information on claims
The software now makes the first pass at clients, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Interview clients and take their calls to provide customer service and obtain information on claims.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Automated service covers routine calls, but someone who has just had a loss often wants a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Staying human
0 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.
Show the other 15 tasks
Transcribing data to worksheets and entering data into computer for use in preparing documents and adjusting accounts
shifting to AIThis is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Transcribe data to worksheets, and enter data into computer for use in preparing documents and adjusting accounts.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Copying data into worksheets and systems is the clearest example of work computers do without help.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Notifying insurance agent and accounting department of policy cancellation
shifting to AIThis is reading one thing and writing another: insurance agent in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Notify insurance agent and accounting department of policy cancellation.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Sending a cancellation notice to the agent and accounts is an automatic step once the record changes.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Processing, preparing and submitting business or government forms, such as submitting applications for coverage to insurance carriers
shifting to AIThis is reading one thing and writing another: business in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Process, prepare, and submit business or government forms, such as submitting applications for coverage to insurance carriers.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Completing and submitting standard business or government forms is structured paperwork software handles reliably.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Composing business correspondence
shifting to AIThis is reading one thing and writing another: business correspondence in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Compose business correspondence for supervisors, managers, and professionals.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Drafting business letters and emails on someone else's behalf is one of the things language software does best.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Entering insurance- and claims-related information into database systems
shifting to AIThis is reading one thing and writing another: insurance- in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Enter insurance- and claims-related information into database systems.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Typing claim details into a database is the plainest form of data entry, and systems already capture most of it.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Modifying, updating
shifting to AIThis is reading one thing and writing another: policies in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Modify, update, or process existing policies and claims to reflect any change in beneficiary, amount of coverage, or type of insurance.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Updating a policy for a change of beneficiary or cover is a standard record change software makes.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Calculating amount of claim
shifting to AIThis is reading one thing and writing another: amount of claim in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Calculate amount of claim.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Working out the claim amount is calculation against policy terms, with an adjuster confirming the result.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing and verifying data, such as age, name, address and principal sum and value of property
shifting to AIThis is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Review and verify data, such as age, name, address, and principal sum and value of property, on insurance applications and policies.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Cross-checking names, ages and values against source records is precisely what checking software is built for.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Calculating premiums, refunds, commissions, adjustments or new reserve requirements, using insurance rate standards
shifting to AIThis is reading one thing and writing another: premiums, refunds, commissions, adjustments or new reserve requirements in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Calculate premiums, refunds, commissions, adjustments, or new reserve requirements, using insurance rate standards.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Premiums, refunds and commissions are calculated from published rate tables, which software applies exactly.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Examining letters from policyholders or agents
shifting to AIThis is reading one thing and writing another: letters in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Examine letters from policyholders or agents, original insurance applications, and other company documents to determine if changes are needed and effects of changes.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading letters and documents to work out what needs changing is document work software handles well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Obtaining computer printout of policy cancellations
shifting to AIThis is reading one thing and writing another: computer printout of policy cancellations in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Obtain computer printout of policy cancellations, or retrieve cancellation cards from file.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: The computer list is instant; pulling paper cancellation cards from a filing cabinet is not.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Organizing or working with detailed office or warehouse records
shifting to AIThis is reading one thing and writing another: detailed office in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Organize or work with detailed office or warehouse records, maintaining files for each policyholder, including policies that are to be reinstated or cancelled.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Digital policyholder files are easy to keep in order, though paper records still need handling.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Paying small claims
shifting to AIThis is reading one thing and writing another: small claims in, a record out. That is the shape today's tools are built for.
importance 5 · SupplementalSource: “Pay small claims.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Small payments run through systems, though someone with authority still stands behind releasing the money.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing customer service
shifting to AIThis is reading one thing and writing another: customer service in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Provide customer service, such as limited instructions on proceeding with claims or referrals to auto repair facilities or local contractors.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Standard guidance on next steps and referrals is well covered by automated service tools.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Collecting initial premiums and issuing receipts
shifting to AIThis is reading one thing and writing another: initial premiums in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Collect initial premiums and issue receipts.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Most premiums are collected electronically with automatic receipts, though cash and checks still pass through hands.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $49,230a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 214,260in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: information in, a record out. The rows above are exactly that shape: posting or attaching information to claim file and corresponding with insured or agent to obtain information or to inform them of account status or changes. What it cannot do is be answerable: information needs a named person the rules will accept, and software cannot be that person. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
The exposed part of your job is the biggest part, and I am not going to dress that up: posting or attaching information to claim file is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 94% of this job's task weight sits in rows the software is already learning, 6% in rows that change shape rather than disappear, and 0% in rows it is nowhere near. That is the position, measured across 25 scored tasks. It is not a forecast about you.
What you have that the software does not is comparing information from application to criteria for policy reinstatement, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of information you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of information, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is posting or attaching information to claim file” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of comparing information from application to criteria for policy reinstatement you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to insurance claims and policy processing clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was insurance sales agents: only about 5% of its durable work is work you already do. I am not going to pretend that is comfortable news: 94% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “compare information from application to criteria for policy reinstatement, and approve reinstatement…” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Insurance Sales Agents
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already contact insured or other involved persons to obtain missing information, and their equivalent is to interview prospective clients to obtain data about their financial resources and needs, the…. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step.
Private Detectives and Investigators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already contact insured or other involved persons to obtain missing information, and their equivalent is to question persons to obtain evidence for cases of divorce, child custody, or missing…. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 35,580 of those jobs against 214,260 of yours (OEWS May 2025), 17% as many seats.
Personal Financial Advisors
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already review insurance policy to determine coverage, and their equivalent is to recommend to clients strategies in cash management, insurance coverage, investment planning, or other…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $105,070 against your $49,230 is 2.13× (OEWS May 2025 (both)), and you would be crossing it holding about 4% of their durable work. A gap that size with an overlap that small is a wish, not a route.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 94% of its task weight, across 25 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: comparing information from application to criteria for policy reinstatement is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Pensions and insurance clerks and assistants is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Pensions and insurance clerks and assistants, Finance officers, Financial administrative occupations n.e.c. and Financial accounts managers. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
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Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for insurance claims and policy processing clerks, and we are not going to point you at the nearest one and call it a fit.
The working behind that
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 94% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for insurance claims and policy processing clerks. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for insurance claims and policy processing clerks launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Insurance Claims and Policy Processing Clerks?
- Not as a job, but it is already doing parts of the work. Across the 25 official task statements scored for Insurance Claims and Policy Processing Clerks (United States, SOC 43-9041), 94% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 79 out of 100 (range 75–83, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Insurance Claims and Policy Processing Clerks” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Post or attach information to claim file” (93/100, very high); “Transmit claims for payment or further investigation” (93/100, very high); “Organize or work with detailed office or warehouse records, using computers to enter, access, search or retrieve data” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Insurance Claims and Policy Processing Clerks” stay human?
- About 0% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Interview clients and take their calls to provide customer service and obtain information on claims” (53/100, partial); “Compare information from application to criteria for policy reinstatement, and approve reinstatement when criteria are met” (56/100, partial); “Collect initial premiums and issue receipts” (64/100, high). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Insurance Claims and Policy Processing Clerks” do about AI?
- Start from the ledger rather than the headline: 94% of this job's weighted core work is exposed, and roughly 0% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Insurance Claims and Policy Processing Clerks calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 25 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt-with-imputed)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.