Insurance Claims Automation: Start With What Your Claims Team Actually Does

Insurance claims automation uses software, from rules and bots to document AI and machine learning, to take steps of a claim off people: intake, data extraction, routing, simple payments, and routine letters. Almost every guide explains it along the claim’s path, from first notice of loss to settlement. But the request on your desk is about a team. The hours you are asked to free up sit in the day of your adjusters, examiners, and claims processing staff, and that day is not shaped like the claim’s path.

That is the gap this article works in. I have spent more than 20 years in operations consulting, including work with insurance carriers such as AIG, Nationwide, and Farmers, and the pattern repeats: claims operations run on output numbers, claims processed and files closed. Those numbers tell you what came out of the process. None of the systems was built to tell you what work it took to get there, and that is exactly what an automation decision needs.

So this guide starts from the claims team’s own work. It covers what claims processing automation does today, what a claims day is actually made of, where each kind of automation lands on that day, what AI changes for adjusters, and the four questions that decide whether you are ready to buy.

Quick Answer

Insurance claims automation uses rules, bots, document AI, and machine learning to take steps of a claim off people, mostly intake, data extraction, routing, and simple payments. It pays off when it targets where the claims team’s hours go: mostly email, phone calls, chasing documents, and re-keying data, not the stages on a lifecycle diagram.

What Insurance Claims Automation Covers Today

Claims automation today covers five stages of the claim: intake, document handling, triage and fraud screening, the decision on simple claims, and payment. Vendors combine robotic process automation (RPA), intelligent document processing, optical character recognition (OCR), machine learning models, and computer vision to do it. The table below shows what usually gets automated at each stage, and whose hours it actually touches.

Read the right-hand column carefully. Most of the automation lands on the steps a claim passes through, and many of those steps take a few minutes of one person’s time. The handler’s day is full of work that never shows up as a stage at all.

If you are being asked to automate claims and want to see where your own team’s hours go before you pick a tool, book a 30-minute strategy call. Bring the target and the vendor shortlist.

What a Claims Team’s Day Is Actually Made Of

A claims team’s day is mostly communication and data handling wrapped around a smaller number of decisions. The US government’s occupational database says so in its own terms. ONET lists 29 tasks for claims adjusters, examiners, and investigators, and in its work-context survey 100% of respondents use email every day, 100% have telephone conversations every day, and 93% write letters and memos every day. For insurance claims and policy processing clerks, ONET lists 25 tasks, 100% use email every day, and 91% spend their time sitting “continually or almost continually.”

What O*NET does not give you, and cannot, is time. It tells you which tasks exist and how often people do them. It does not tell you how many hours a week your handlers spend on each one. That number is what an automation business case rests on, and it is missing from almost every plan I see.

The occupation is large. The Bureau of Labor Statistics counts 389,700 claims adjusters, appraisers, examiners, and investigators in the US in 2025. That is a lot of working days built mostly from email, calls, and files.

The five kinds of time in a claims day

Sort any handler’s day by what the time is for, rather than by the stage of the claim, and five kinds of time come out:

• Deciding: coverage, liability, reserve, and settlement calls. The judgment the role exists for, and the part that binds the carrier.
• Chasing: getting what the file is missing. Police reports, medical records, repair estimates, a signed form, a callback from a contractor or a witness.
• Moving: re-keying data from a PDF or an email into the claims system, posting notes, entering payments and reserves, copying between systems that do not talk to each other.
• Explaining: status calls, emails to claimants and agents, letters that say what happens next.
• Checking: verifying documents, reviewing bills, confirming the file is complete before it moves on.

The O*NET task list maps onto these five without strain. “Examine claims forms and other records to determine insurance coverage” is deciding. “Contact or interview claimants, doctors, medical specialists, or employers to get additional information” is chasing. “Enter claim payments, reserves and new claims on computer system” is moving.

Here is what that can look like in practice. Take a desk adjuster on a property team, call him Marcus. This is a composite with illustrative numbers, written to show the shape of the day, not data from any client.

Marcus opens 40 emails before ten. Six are documents he asked for last week, which he saves to the file and keys into the claim. Eleven are claimants or agents asking where their claim stands. Two contain the information he needs to make a coverage decision, and he makes both before lunch, in about 25 minutes.

The rest of his morning is chasing, moving, and explaining. His team’s output report shows two decisions and six documents received. It shows nothing of the hours around them.

Where Claims Processing Automation Lands on That Day

Claims processing automation is strongest on moving and checking, partial on chasing and explaining, and supporting at most on deciding. That is the useful way to read any vendor pitch: ask which kind of time it takes, and from whom.

Two things follow from this table.

First, even good automation leaves a remainder. EY’s case study of a Nordic insurer, which automated the reading of bills, pharmacy receipts, and medical documents, reports that “70% of the documents fed into the system are correctly extracted and interpreted.” That is a real result. It also means the documents in the other 30% still come back to a person, and they are the messy ones.

Second, the biggest kinds of time for most handlers, chasing and explaining, are the ones the stage-based guides cover least. A plan that automates intake and document extraction can succeed on its own terms and still leave the handlers’ week almost unchanged, because their week was mostly spent on the phone and in the inbox.

This is the same discipline we describe in how to know what to automate in operations, applied to one team. For claims specifically, the AI operations roadmap for insurance sets out the order to automate in and the regulatory overlay that comes with it, so I will not repeat it here.

AI in Claims Processing: What Changes for Adjusters

AI in claims processing changes the moving, checking and explaining parts of an adjuster’s day first, and the deciding part last. The Bureau of Labor Statistics puts it plainly: “Technology is expected to automate some of the tasks that these workers currently perform. For example, computer software can evaluate photographs of damaged property and calculate an estimated claim amount.” It projects the occupation to decline 6% from 2025 to 2035.

Spread over 10 years, a 6% decline means a smaller team with less data work in it.

The newer generative tools push in the same direction: summarizing a long file before a handler picks it up, drafting the letter or the status email, pulling the right clause from a policy. Each of those takes minutes off explaining and checking. None of them makes a coverage call that a carrier would want to defend without a person behind it.

So for a claims leader, the useful question is which of your people’s hours AI will take, how many, and whether those hours come back to you as capacity you can actually use.

Claims Processing Automation Readiness: Four Questions for Your Claims Team

Claims processing automation readiness comes down to your evidence. Before you sign, you should be able to answer four questions about your own claims team, with data rather than estimates. The full measurement list for a carrier is in workforce intelligence for insurance; these are the four that decide a claims automation purchase.

1. How many hours a week go to each kind of time, by role? Not by stage of the claim, by person. Deciding, chasing, moving, explaining, and checking, for desk adjusters, examiners, and processing staff separately. Without this, every savings figure in the business case is a vendor benchmark applied to your headcount.

2. Where does the work physically happen? If the chasing and explaining live in individual inboxes and on the phone, a claims system log will not show them, and an automation that reads the claims system will not reach them.

We have seen this outside insurance too. In a shared services engagement for a payments company, more than 90% of customer requests arrived by email to individual mailboxes, so nobody could see workload, quality, or service levels. Our consultants started with 868 side-by-side observations, then moved the work into a single intake system, and the client saved $1.2M in 23 weeks.

That was a consulting engagement, done by hand, not our platform. The lesson carries: find where the work lives before you automate it.

3. Will the freed hours land in one place? Ten minutes saved for each of 60 handlers is 10 hours a day spread across the floor. It rarely becomes a role you can redeploy.

We go into this in what to measure before a workflow automation project. For claims, it means asking whether an automation removes a block of work from a few people or a sliver from everyone.

4. Does the same task take the same time across handlers? Usually not, and the gap is information. As our COO Andy puts it, we “could see the same activity captured and classified by different … workflows. And that’s where the opportunity is.”

Sometimes the slow version is a workaround that a fix would remove. Sometimes it is a handler dealing with cases the fast version skips. You need to know which before you automate either.

Most claims leaders can answer none of these from the data they hold today. What they hold is output data. I have watched experienced operations leaders rebuild the work by hand from it, exporting reports, building macros, trying to work out how many touches it takes to close a file. It is a daily pain, and now the stakes are higher, because those spreadsheets are being used to size automation and headcount decisions.

If those four questions are the ones you cannot answer yet, a 30-minute strategy call is a good place to start. We will talk through your claims operation and what a baseline would show.

How We Measure a Claims Team’s Day

We measure the claims day directly, at the activity level, in a fixed 90-day engagement. A lightweight desktop client captures the region around each click, 500 to 3,000 captures per person per day, with no keystrokes and no full screens. Our Patent Pending vision AI then classifies each capture by what the person is doing, so you see “entering a repair estimate into the claim file” rather than “in the claims system.” Our approach explains the capture, classify, and insight steps in detail.

The output is time allocation by activity, application drill-downs, workflow maps, day narratives, and an automation score for each activity, for every person in scope. You can see what that output looks like. It is the five kinds of time, measured rather than guessed, with the automation potential of each.

A few things to know before you call:

• Scope: a minimum of 50 employees in the target operations area, and your infrastructure on Azure or AWS. Initial findings land at weeks three to four.
• Privacy: click-region capture only, the customer owns the data, AES-256 encryption at rest and TLS 1.3 in transit, with a self-hosted option. The platform page covers the architecture, and we explain where we draw the line in workforce intelligence vs. employee monitoring.
• What we do not do: we do not implement automation, and we do not recommend a specific claims automation vendor. We tell you which work is worth automating, so the vendor conversation starts from your data.

Measuring before changing is how our team has always worked. In the Nationwide back office case study, our consultants observed the work before standardizing it. The platform does the observing automatically now, for every person in scope, every day.

Frequently Asked Questions

Is AI going to replace claims adjusters?

Not as a job, on the evidence so far. The US Bureau of Labor Statistics counts 389,700 claims adjusters, appraisers, examiners and investigators in 2025 and projects a 6% decline by 2035, partly because software can already evaluate photographs of damaged property and estimate a claim amount. That is a smaller team doing less data work, not a claims operation without people. Coverage, liability and settlement decisions still sit with a person.

Do insurance companies use AI to process claims?

Yes, mostly on the data-heavy steps: reading documents, extracting fields, routing claims, flagging possible fraud and estimating damage from photos. EY’s write-up of a Nordic insurer reports that 70% of the documents fed into its new system are correctly extracted and interpreted, which shows both how far the technology has come and how much still lands on a person.

Who is a claim processor?

A claim processor, or insurance claims and policy processing clerk, prepares claim forms and checks them for completeness, posts information to the claim file, contacts the insured for missing details, calculates claim amounts, pays small claims and passes the rest on for payment or investigation. O*NET lists 25 tasks for the role. It sits beside the adjusters and examiners who make the coverage and settlement decisions.

What are the four phases of the claim process?

Most carriers describe the claim in four phases: the report of the loss (first notice of loss), the investigation, the evaluation and decision on coverage and amount, and the settlement and payment. Each phase has its own mix of data work and judgment, which is why automation lands unevenly across them.

What is an example of intelligent automation in claims?

A common one: document AI reads a repair estimate or medical bill that arrives by email, extracts the amounts and dates, a bot keys them into the claims system, and a rule routes the claim to the right handler or pays it if it is small and clean. The handler sees the file only when something does not fit.

What is claims management software?

Claims management software is the system of record for a claim: it holds the file, the reserves, the payments, the notes and the diary of follow-ups, and it moves the claim from report to closure. Most claims automation either sits inside it or feeds it. It records what happened to the claim, not how the claims team spent its time getting there.

Start With the Day, Then Pick the Tool

Insurance claims automation works when it is aimed at where the claims team’s hours actually go. The lifecycle diagrams describe the claim. Your mandate is about the people handling it, and their day is mostly chasing, moving, and explaining around a smaller core of decisions.

Measure that day first, by role and by kind of time, and the automation choice gets much easier: you know which hours a tool would take, from whom, and whether they come back as capacity you can use.

Book a 30-minute strategy call with me. We will look at your claims operation, what you are being asked to automate, and what a baseline of your team’s day would show. No pitch, and no obligation.