TPAs handle rising claim volumes without adding headcount by automating the repetitive, document-heavy work — intake, extraction, and validation — so the same team clears far more claims per day. The gains are large and measurable. One specialty insurer automated more than 90% of its claim intake and processed claims more than 10x faster after deploying FurtherAI, turning its claims operation from a growth bottleneck into a function that scales with the book.

This guide leads with the throughput and cost-per-claim outcomes, then walks through how to get them. It's part of our series for third-party administrators (TPAs), alongside the parent guide to the best AI for claims processing and adjudication at TPAs and our deep dive on no-code claims adjudication and low-IT-lift deployment.

Key takeaways

• Volume is rising while the workforce shrinks. U.S. employment of claims adjusters, appraisers, examiners, and investigators is projected to decline 5% from 2024 to 2034, per the U.S. Bureau of Labor Statistics, so hiring your way out isn't realistic.
• Automation lifts throughput per person. Automating intake and extraction lets the same team clear more claims, because the repetitive work that used to consume hours per file can now be handled by the platform.
• The outcomes are documented. One specialty insurer hit more than 90% intake automation, more than 10x faster processing, and over $360K in annual savings — a 568% return on investment — per our claims processing case study.
• Cost per claim falls as volume climbs. Because automation cost doesn't scale linearly with headcount, each additional claim costs less to process than it did under a manual model.
• Start with the highest-volume workflow. Pilot one claim type, prove the numbers against your own history, then extend the same approach across clients and lines.

The volume-versus-headcount squeeze

TPAs are caught between two trends moving in opposite directions. On the demand side, claims administration is the largest service line in the third-party administration market, at 40.76% of service revenue in 2025, and it's a growing one, according to Research and Markets. As carriers and employers keep outsourcing claims work, the volume each TPA team has to clear climbs with it.

At the same time, the people who do that work are getting scarcer. The U.S. Bureau of Labor Statistics projects employment of claims adjusters, appraisers, examiners, and investigators to fall 5% through 2034, with roughly 21,600 openings a year arising mostly from workers who retire or leave the field. The BLS points to automation as the reason productivity can rise even as headcount falls.

For a TPA, that math rules out the obvious answer. You can't reliably hire enough experienced adjusters to match rising volume, and even if you could, adding headcount raises your cost per claim and squeezes already-thin margins. The way through is to raise how much each person on your team can handle.

The outcome that matters: throughput and cost per claim

The clearest proof comes from a specialty insurer that ran more than 3,000 claims a year with a near-entirely manual intake process, spending about 2.5 hours of human time per claim. After deploying FurtherAI's configured claim intake workflow, the results reshaped its unit economics, as detailed in our claims processing case study.

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Two numbers carry the throughput story. Automating more than 90% of intake removed about 7,500 labor hours a year, and processing ran more than 10x faster, so the same team could absorb far more claims without new hires. On cost, more than $360K saved across 3,000-plus claims works out to roughly $120 per claim, a figure that keeps improving as volume grows, because the automation handles additional claims without a matching rise in labor cost.

The strategic result mattered more than any single metric. This insurer had posted three straight years of more than 20% premium growth and expanded from five insurance programs to 10, and its manual claims operation had become the bottleneck threatening that growth. After automation, the claims organization was no longer the constraint, and the company kept scaling.

How to scale volume without adding headcount

Raising throughput per person comes down to removing manual effort from the highest-volume steps and reserving your experienced adjusters for the work that needs judgment. A practical sequence:

1. Target the highest-volume workflow first. Claim intake and document extraction are usually the heaviest repetitive load, which makes them the biggest source of recoverable hours.
2. Automate extraction and validation. Let the platform read incoming documents, extract field-level data, and check completeness against your requirements, so claims arrive ready to work rather than half-formed.
3. Enable straight-through processing for simple claims. Route clean, low-complexity claims through automatically, and hold the exceptions for human review.
4. Keep adjusters on judgment work. Direct your team's time to complex, high-severity, or ambiguous files where experience changes the outcome.
5. Measure and expand. Track automation rate, error rate, and processing time on the pilot, validate against your own historical claims, then extend the same configuration to the next client or line.

This staged approach is how the specialty insurer mentioned above moved intake automation from near zero to more than 90% without disrupting live operations. It proved one workflow, then scaled the model.

The broader industry evidence points the same way. In a widely cited transformation, McKinsey's work with Aviva used more than 80 AI models to cut liability-assessment time on complex claims by 23 days and improve routing accuracy by 30%, showing that automation raises both speed and quality when it's applied across the workflow rather than to a single task.

What to look for in a platform that scales

A platform that raises throughput without adding risk should be insurance-specific, integrate with your existing claims systems, and keep humans in the loop on complex claims with a full audit trail behind every decision. For TPAs specifically, it also needs to onboard new clients through configuration rather than a fresh build, so each new program adds volume without adding a project.

For the complete evaluation framework across platforms, see the parent guide. If you're deciding whether to build that capability in-house or buy it, our guide on build versus buy for TPAs walks through the trade-offs. And if you're specifically interested in no-code claims adjudication automation with no code/low IT lift, see our dedicated article on the topic.

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REFERENCES

^{Bureau of Labor Statistics. "Claims Adjusters, Appraisers, Examiners, and Investigators: Occupational Outlook Handbook." U.S. Bureau of Labor Statistics. bls.gov}

^{McKinsey & Company. "Aviva: Rewiring the Insurance Claims Journey with AI." McKinsey & Company. mckinsey.com}

^{Research and Markets. "Insurance Third Party Administrators Market." Research and Markets. researchandmarkets.com}

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DISCLAIMER 

This article is for general informational purposes only and does not constitute legal, regulatory, compliance, underwriting, or other professional advice. The content reflects information available as of the date of publication, and FurtherAI undertakes no obligation to update it as laws, regulations, or AI technologies evolve. 

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