Will AI Replace Insurance Claims Clerks? 85% of Data Entry Is Already Automated

If you process insurance claims for a living, you have probably already noticed that the software is doing more and more of your job. The form that used to take you fifteen minutes to review? The system now pre-fills 85% of the fields. That is not a coincidence -- it is a preview of where this entire profession is heading.

[Fact] According to the Anthropic Labor Market Report (2026), insurance claims and policy processing clerks face an overall AI exposure of 67% with an automation risk of 65%. There are 283,600 people in this role in the United States, earning a median annual wage of $45,990. The Bureau of Labor Statistics projects a -5% decline in employment through 2034. And the automation mode classification is stark: automate, not augment.

That single word -- automate -- tells you what the data sees coming.

Where AI Has Already Taken Over

Entering and Updating Policyholder Information: 85% Automation

[Fact] This is the most automated task in insurance claims processing, and it is also the most intuitive. Data entry is what AI was essentially built for. Optical character recognition reads incoming documents. Natural language processing extracts key fields -- names, policy numbers, dates, amounts. Machine learning validates entries against existing records and flags inconsistencies. The clerk's role has shifted from entering data to reviewing what the system already entered.

[Claim] The remaining 15% exists not because AI cannot do it, but because of edge cases -- handwritten notes from older policyholders, damaged documents from disaster claims, complex multi-party policies where the system cannot confidently determine which entity goes in which field. These exceptions keep humans in the loop, but they represent a shrinking share of total volume.

Reviewing and Processing Claims Submissions: 80% Automation

[Fact] Insurtech platforms have transformed claims intake. AI systems now triage incoming claims, check them against policy terms, flag potential fraud indicators, and route them to the appropriate processing track -- all before a human sees the file. Straightforward claims -- a windshield replacement under comprehensive coverage, a standard medical reimbursement -- can flow through the entire system without human intervention.

The 20% that still requires human review involves ambiguous claims: those that fall near policy limits, contain potential subrogation opportunities, or involve coverage disputes that require interpretation rather than simple rule application.

Calculating Premiums and Adjusting Policy Terms: 76% Automation

[Fact] Premium calculation was one of the earliest insurance functions to be automated. Actuarial models feed directly into pricing algorithms, and AI has added the ability to incorporate non-traditional data sources -- telematics data for auto insurance, IoT sensor data for property insurance, wearable data for health insurance. The clerk's role in premium calculation has largely been reduced to handling overrides and exceptions.

Corresponding with Policyholders and Agents: 62% Automation

[Fact] This is the most human-dependent task remaining in claims processing, and it still sits at 62% automation. AI-powered chatbots and automated email systems handle routine correspondence -- claim status updates, documentation requests, payment confirmations. But when a policyholder is upset about a denied claim, confused about their coverage, or dealing with a catastrophic loss, the conversation requires human empathy and judgment.

[Claim] This is the lifeline for claims clerks who want to stay relevant. The ability to handle difficult conversations, de-escalate frustrated policyholders, and explain complex policy terms in plain language is the most automation-resistant skill in the role.

The Timeline: Accelerating Fast

[Fact] The data from 2023 to 2025 shows how quickly this transformation is moving. In 2023, overall exposure was 56% with 31% observed adoption. By 2024, it jumped to 62% with 36% adoption. In 2025, exposure hit 67% with 41% actual implementation. Every single year, both exposure and adoption have climbed significantly.

[Estimate] Projections through 2028 show exposure reaching 80% with automation risk hitting 78%. The theoretical ceiling is 93%, meaning that in principle, nearly everything a claims clerk does could be handled by AI. The only question is how quickly insurers will implement the technology and what friction -- regulatory, organizational, or customer-facing -- slows the adoption curve.

Compare this trajectory to insurance policy clerks, who face an even steeper path: 72% automation risk in 2025 climbing to 85% by 2028. Or to insurance appraisers, whose physical inspection work provides a natural floor that desk-based clerks do not have.

Why This Matters for 283,600 Workers

[Fact] This is not a niche profession. Nearly 284,000 people process insurance claims in the U.S., and the -5% BLS decline means roughly 14,000 fewer positions over the next decade. That is the net number -- the actual displacement will be higher, partially offset by industry growth and new policy volume.

[Claim] The "automate" classification is the key signal here. Unlike internal auditors who are classified as "augment" (AI helps them do their job better), claims clerks are classified as "automate" (AI replaces what they do). This distinction matters enormously for career planning.

What Insurance Claims Clerks Should Do Now

1. Move Up the Complexity Ladder

Routine claims processing is being automated. Complex claims adjudication is not. If you can develop expertise in subrogation, coverage disputes, or catastrophe claims -- the cases where AI flags uncertainty and a human must decide -- you position yourself for the roles that survive.

2. Develop Customer Relations Skills

The 62% automation in correspondence still leaves 38% that requires a human touch. If you excel at handling difficult policyholder conversations, you have a skill that will remain valuable even as everything else around it is automated.

3. Learn the Technology Stack

Understand the AI tools your company uses for claims processing. The clerks who can configure rules, train the system on new claim types, and troubleshoot when automation breaks down become system administrators rather than data entry operators. That is a fundamentally different career trajectory.

4. Consider Career Pivots Within Insurance

The insurance industry is not shrinking -- it is restructuring. Roles in claims investigation, fraud detection, customer success, and insurtech product development are growing even as processing roles decline. Your industry knowledge is transferable; the question is whether you apply it to a growing function or a shrinking one.

For complete exposure data and task-level metrics, visit the Insurance Claims Clerks data page.

The Bottom Line

Insurance claims clerks face one of the more challenging AI outlooks among administrative professions. With 67% exposure, 65% automation risk, an "automate" classification, and a -5% employment decline projected, the data is clear: this role is being systematically automated. The 85% data entry automation and 80% claims processing automation leave little ambiguity about the direction.

But 283,600 people still do this work, and the transition will take years, not months. The window to adapt is open. The question is whether you use it to climb toward complex claims work and customer relations -- the tasks AI still struggles with -- or wait for the next automation wave to reach your desk.

This analysis was produced with AI assistance, drawing on data from the Anthropic Labor Market Report (2026), Bureau of Labor Statistics projections (2024-2034), and industry research. All statistics have been verified against primary sources.

Sources

• Anthropic. "The Anthropic Labor Market Impact Report." 2026.
• U.S. Bureau of Labor Statistics. "Occupational Outlook Handbook: Insurance Claims and Policy Processing Clerks." 2024-2034.
• Eloundou, T. et al. "GPTs are GPTs." arXiv, 2023.

Update History

• 2026-03-30: Initial publication with 2023-2025 actual data and 2026-2028 projections.