What This Top U.S. Insurer Found Hiding in 70,000 Claims

How a top-tier U.S. insurance carrier partnered with Ashling to finally see and fix what was quietly draining its claims management operation.

The data to improve most business operations already exists inside an organization. The hard part is making sense of it: where time, money, and customer trust are being lost.

When a customer files an insurance claim, whether a car accident or a damaged roof, they expect it to be handled quickly, accurately, and without having to call three times to find out its status. On the other side of that interaction is a complex operation involving dozens of steps, multiple teams, and systems that often don't interact.

Most large insurers know their claims process has room for improvement. What they rarely have is a clear, detailed picture of exactly where things slow down, where money is being lost, and which problems are worth fixing first. That’s the gap Ashling was brought in to close.

One of the largest and most diversified insurance providers in the United States, with tens of thousands of employees and multi-billion-dollar revenue, processed tens of thousands of claim cases each year. By most measures, the business was performing well, but persistent efficiency problems lay beneath the surface. As sustainable, high-quality growth became a priority, the client began to ask; “where exactly are these problems coming from?”, and “what’s causing them?”

That is what led to a broader claims management optimization effort. Early in the engagement, one hurdle became clear: to make confident decisions about where to invest and what to improve; the team first needed a data-backed understanding of how the claims process was actually running day to day.

The data was there, and the client already had a process intelligence tool in place; Celonis. What was missing was the right expertise to dig into it, make sense of it, and translate it into clear recommendations that business leaders could act on.

Ashling brought the technical and analytical depth to uncover the root causes of claims process deviation using workflow and activity-level data, and to quantify their impact. From there, the team translated those findings into a prioritized set of automation opportunities with the greatest potential impact.

Ashling worked inside the client's Celonis environment, analyzing over 70,000 insurance claims from the previous 12 months. The work began with a confirmatory analysis of 12 claim segments within Auto and Property lines of business, validating the areas that leadership suspected had friction. From there, 60+ use cases were identified for further exploration across their respective claim lifecycle stage: first notice of loss (FNOL), intake, assignment, investigation, settlement and close.

Ashling then developed a scoring methodology to evaluate claim quality of each use case. The team looked at metrics like how often customers complained, how often a claim had to be re-opened after being closed, and how accurately the initial financial estimate matched the eventual payout. Segments were scored by weighting claim volume, payment value, and quality metrics, adjusting for claim complexity. Within priority segments, phase-level cycle times and variability pin-pointed where delays drove the most impact.

Finally, insights were synthesized into a prioritized list of opportunities ranked by value. Five clear use cases emerged from the discovery diagnostic.

1. Claim Intake Digitization: More than 94% of Auto claims were still reported by phone, even though they are low-complexity and highly consistent once intake is complete, making them perfect candidates for automated intake. Shifting phone intake from 94% to 70% (an industry standard target) could eliminate nearly 20,000 call center contacts annually by directing customers to self-service channels.
2. Subrogation Efficiency: The client spent too much effort assessing claims for subrogation that never lead to recovery. Today, 61% of claims are assessed, but only 8% result in recovery. By leveraging predictive analytics and machine learning tools to highlight claims unlikely to result in meaningful recovery, the client could avoid about 22,000 low-value investigations per year, cut investigation time from 136 hours to under 60, and reduce the financial exposure in their backlog by over $100M.
3. Adjuster Workload Prioritization: The simplest claims were also identified as the largest bottleneck, with non-investigation claims sitting in adjuster queues for 19 days, while more complex claims were prioritized first. Flipping this order, and prioritizing the “low hanging fruit” could speed up resolution for 12,500 claims annually, and reduce the complaint rate by over 50%.

One of the most counterintuitive findings was in how adjusters were spending their time. Simple, straightforward claims, the kind that could realistically be closed in a day, were sitting idle in queues for an average of 19 days. Meanwhile, complex cases that required investigation were being picked up within an hour, resulting in 2x higher complaint rate.

There was a similar unexpected pattern in how property claims were being filed online. Digital intake is usually considered a sign of efficiency where customers self-service reduces call center load. But the data showed that the online intake form wasn't capturing enough detail for accurate initial assessments. The digital channel, which handled 93% of this claim type, was driving some of the worst outcomes in the portfolio. Claims were being closed too early, then re-opened, and financial reserves were being set at the wrong level from the start.

The engagement delivered what the broader program needed: a clear, evidence-based picture of where the claims operation was losing time, money, and customer trust, along with a prioritized roadmap for addressing it using a combination of AI, automation, and process re-engineering.

Across both lines of business, Ashling built a comprehensive baseline of claims behavior that the organization had not previously had in structured form. That baseline alone changed the nature of the conversation about where to invest.

The findings report and automation opportunity view gave our client the quantitative foundation needed to facilitate an executive prioritization conversation with confidence. By showing the level of insight available through the client’s process intelligence platform and identifying targeted automation opportunities, Ashling helped lay the foundation for a broader roadmap built on data-driven prioritization.

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