Case Study: How an Insurance Company Solved the 6x Performance Gap Between Claims Handlers

The 6x Performance Gap Nobody Could Explain

A mid-market insurance services company had a problem every claims operation has but few can quantify.

Two handlers processing identical claims. Same claim type, same complexity, same systems. Agent A completes it in 20 minutes. Agent B takes 2 hours.

Not occasionally. Consistently.

One handler processed 15 claims per day at 95% quality. Another handled 6 claims per day at 85% quality. The performance variance was expensive and unexplainable.

Leadership knew the gap existed. They saw it in the numbers. Average handle time, claims closed per day, quality scores. But they couldn't answer the fundamental question: what do the top performers actually do differently?

Not "they're more experienced." Not "they're more motivated." What specific steps, in what sequence, using which systems, creates the 6x performance difference?

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The company processed thousands of claims monthly across automotive and property damage. 30 to 40 claims handlers managing end-to-end workflows: intake, verification, supplier coordination, settlement. High-volume, low-complexity work spread across disconnected systems. AWS Connect for telephony, Gmail for email, Twilio for SMS, a constantly-changing claims portal.

The offshore operations team had zero visibility into execution. The only way to check work quality was manual case-by-case review. SOPs were outdated the moment they were published because the portal changed constantly. Leadership couldn't measure if new tools or automation were actually being adopted. They couldn't set fair KPIs because different customer portfolios required different work patterns. Email-heavy accounts versus portal-based accounts created natural variance that made performance comparisons meaningless.

The Head of Operations was accountable for handler productivity and quality. The CEO and CFO had given a mandate: scale revenue without linear headcount growth. The Data and AI function was tasked with building an automation roadmap but needed to prove ROI on implementations.

They needed visibility. Not into outcomes. Into execution.

What You Can't See, You Can't Fix

Traditional approaches to this problem don't work.

You can measure average handle time. That tells you there's variance. It doesn't tell you why. You can review cases manually. That's expensive, slow, and captures a tiny sample that might not represent the real patterns. You can update training materials and SOPs. But if the training doesn't match what top performers actually do, you're teaching the wrong thing.

Most insurance operations approach performance management backwards. They measure outcomes (claims closed, handle time, quality scores) and assume the execution that drives those outcomes is consistent. It's not.

Handler A closes claims fast because they use 8 specific steps with portal shortcuts. Handler B takes longer because they use 23 steps with redundant manual checks. Both get to the same outcome. One is 6x faster.

Without execution visibility, every performance conversation is guessing. "You need to work faster" doesn't help if the handler doesn't know which of their 23 steps are unnecessary. "Follow the SOP" doesn't help if the SOP doesn't match what the top performers actually do.

The company needed to understand why identical claims took 20 minutes versus 2 hours. They needed to auto-generate and maintain SOPs that reflected actual best practices, not outdated documentation. They needed fair KPIs that accounted for portfolio complexity differences. They needed to identify which handlers were top performers and what they did differently. They needed to measure tool adoption and automation effectiveness in real-time. They needed to scale QA without manual case review.

They needed to see the work, not just the results.

Two Weeks to Complete Execution Visibility

Fluency deployed to the claims handling team in one hour. No integrations needed. It worked across AWS Connect, Gmail, Twilio, and the claims portal simultaneously.

Two weeks later, they had complete visibility into claims handling workflows.

Fluency automatically discovered all claim process variants with execution metrics. Cycle time, steps taken, system usage, rework patterns. The claims handlers continued working exactly as they always had. Fluency captured the operational reality.

The discovery: Handlers were executing the same claim types with dramatically different methods.

Agent A, the 20-minute handler, used 8 steps. Agent B, the 2-hour handler, used 23 steps with redundant checks. Agent A leveraged portal shortcuts that eliminated manual data entry. Agent B manually typed everything. The specific step-by-step differences were mapped and quantified.

This wasn't about effort or motivation. Agent B was working harder, doing more steps, being more thorough. They were just following an inefficient process that nobody had visibility into until now.

Fluency showed the operations team exactly what the top performers did differently. Not in aggregate. Not in theory. Step by step, system by system, with time stamps and success rates.

Standardizing Excellence Instead of Averaging Mediocrity

The operations team now had something they'd never had before: a complete map of how work actually happened.

They could see which handlers used the new automation tools and which ignored them. They could see which processes caused rework versus which got it right the first time. They could see where time actually went in a 2-hour claim versus a 20-minute claim.

Most importantly, they could see the exact methods their top performers used.

Fluency didn't just identify the performance gap. It captured the specific workflows that created superior performance. The 8-step sequence. The portal shortcuts. The verification methods that caught errors early instead of late. The supplier coordination patterns that eliminated back-and-forth delays.

The operations team standardized the top performer methods automatically. Best practices were promoted to team SOPs. Not as documentation that would go stale, but as living operational data that updated as the top performers refined their methods.

They built individual handler productivity dashboards showing time on calls, emails, portal work, and claim types handled. They created performance baselines that accounted for portfolio complexity. Email-heavy customer accounts versus portal-based accounts had different execution requirements. Now the KPIs reflected that reality instead of comparing incomparable workloads.

They measured tool adoption in real-time. Which handlers used the new automation? Which worked around it? What was the productivity impact? No more deploying tools and hoping they helped. Now they had data.

They implemented smart triggers for quality assurance. Fluency auto-flagged non-compliance without manual review. Missing required verification steps? System flags it immediately. Handler skipping a critical process stage? Operations knows in real-time, not weeks later during case review.

The operations team finally had data to set fair KPIs and have evidence-based performance conversations. Not "you need to work faster." But "you're using 23 steps. Here are the 8 steps top performers use. Let's get you there."

What Changed After Visibility

Complete execution visibility across claims handling operations.

Leadership could identify top performers and scale their methods across the team. Not by asking top performers to train others. By capturing their exact execution patterns and making those patterns the standard.

Auto-generated SOPs that updated as processes evolved. When the claims portal changed, top performers adapted their workflows. Fluency captured those adaptations automatically. The SOPs stayed current without manual documentation work.

Fair, portfolio-adjusted performance metrics for the entire team. Handlers with email-heavy portfolios weren't compared against handlers with portal-based portfolios. KPIs reflected the actual work complexity, not just volume.

Data-driven training. New hires learned from top performer patterns, not outdated manuals. Onboarding showed them the exact 8-step sequence that worked, not the theoretical 15-step process in the documentation.

Measurable tool adoption and automation ROI. Every new tool deployment came with immediate visibility into who used it, how they used it, and whether it actually improved productivity. No more black box implementations.

Quality assurance at scale through automated compliance monitoring. Instead of manually reviewing a sample of cases, operations had real-time visibility into process adherence across every claim. QA shifted from retrospective audits to proactive coaching.

The foundation to scale capacity 40% without proportional headcount growth. By standardizing top performer methods and eliminating inefficient variants, average handler productivity moved toward top performer levels. More claims processed with the same team size.

Traditional approach: 6-month data warehouse project, multiple API integrations, custom dashboards, ongoing maintenance, expensive and fragile.

Fluency approach: 1-hour deployment, 2 weeks to insight, zero integration work, immediate ROI.

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The Performance Variance Trap

Every insurance operation has performance variance. It's accepted as normal. "Some handlers are just better than others."

But performance variance is expensive.

If your top handler processes 15 claims per day at 95% quality and your median handler does 8 claims per day at 85% quality, that's not a people problem. That's a process visibility problem.

The median handler isn't lazy or incompetent. They're executing a different process. Usually a longer, less efficient process they invented themselves or learned from someone who invented it. They don't know there's a better way because the better way is invisible.

The top handler didn't get lucky. They figured out an efficient workflow through trial and error, or they learned it from another top performer, or they discovered portal shortcuts that others don't know exist. That knowledge stays locked in their execution. It doesn't spread because it's never captured.

Traditional performance management tries to close this gap through training and coaching. But the training is based on documented processes that don't match what top performers actually do. And coaching is based on outcomes, not execution, so it amounts to "work faster" or "be more careful" without showing how.

The performance variance persists. And when you scale the team, you scale the variance. Half your new hires will figure out efficient workflows. Half won't. The expensive randomness continues.

What Execution Visibility Actually Enables

With execution visibility, performance management becomes precise.

You're not guessing why Agent A is faster than Agent B. You can see the exact differences in their workflows. You can show Agent B the specific steps Agent A uses and measure whether adopting those steps improves their performance.

You're not deploying new tools blindly. You can see which handlers adopt them, which work around them, and whether adoption correlates with productivity improvement. You can identify why some handlers benefit from the tool while others don't.

You're not writing SOPs based on theory. You're capturing actual top performer workflows and making those the standard. When processes change, top performers adapt first. Fluency captures their adaptations. The SOPs stay current automatically.

You're not setting arbitrary KPIs. You're building performance baselines from actual execution data, accounting for portfolio differences, and measuring improvement based on workflow efficiency rather than just volume.

You're not doing manual QA on random case samples. You're monitoring every claim for process adherence in real-time and flagging exceptions automatically.

This is what it means to manage operations with data instead of hope.

The Question That Exposes Everything

Your best claims handler closes a claim in 20 minutes at 98% quality. Your median handler takes 2 hours at 82% quality.

Can you show me, right now, exactly what the top performer does differently?

Not "they're more experienced." Not "they're more motivated." Not "they're just better."

What do they actually do differently? Which specific steps? In which systems? In what sequence? With what timing?

If you can't pull up that data in 30 seconds, you're not managing claims operations. You're hoping performance improves.

And every new hire you add is another expensive bet that compounds the variance instead of closing it.

Most insurance operations can't answer this question. They measure outcomes and assume execution is consistent. It's not. The execution variance is massive, expensive, and fixable.

But only if you can see it.

The Path Forward

You don't need to fix your entire claims operation to get value from execution visibility.

Start with one process that matters. The one where performance varies inexplicably. The one where you know top performers exist but can't explain what they do differently. The one where you're about to hire more people but suspect efficiency gains would solve the capacity problem.

Make that process visible. See what's actually happening. Find what's already working.

Then standardize, measure, and scale.

That's what this insurance company did. They started with claims handling. The pattern was immediate: significant performance variance, hidden best practices, clear opportunities for standardization.

Now they're expanding to other operations. Underwriting workflows, customer service processes, fraud investigation methods. Each time, the same pattern emerges: variance exists, top performers have figured out better ways, those ways stay invisible until Fluency captures them.

You can't scale excellence you can't see. But once you can see it, scaling becomes systematic instead of random.

The question isn't whether your operation has performance variance. It does. The question is whether you can see it clearly enough to close it.

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Ready to see why your top performers work differently?

Fluency delivers execution visibility in weeks, not quarters. No integrations, no data warehouses, no guesswork.

See how leading insurance operations close the performance gap with data.