What Is Process Intelligence for Insurance Claims

What Is Process Intelligence for Insurance Claims?

Most insurance organizations run claims across five to ten applications. When cycle times stretch or denial rates climb, no single system tells you why. But process intelligence can help close that gap. 

Using process intelligence for insurance claimsallows insurers to analyzeevery step of the claims workflow, from intake and triage through assessment, adjudication, and payment, across all systems and teams. It gives operations leaders a better picture of how claims work flows, rather than how it was designed to flow.

How does process intelligence differ from process mining for insurance claims?

This is the most common question from claims VPs evaluating operational intelligence tools. The answer comes down to what each technology can actually observe.

Process mining reads structured event logs from integrated systems. It works well for simple, linear workflows where every step happens inside a single platform. But insurance claims rarely fit that description.

A typical claims workflow spans mainframes, custom adjudication platforms, case management tools, email, and Excel, with adjusters toggling between applications many times per case. Process mining only sees the steps captured in event logs. It is blind to everything in between.

Process intelligence ideally captures every application interaction, every system switch, every manual workaround, and every exception step directly at the desktop level, leaving fewer blind spots in the end-to-end process.

That distinction matters for insurance because the most costly inefficiencies in claims often live in the details that process mining misses. For example, one major insurer using Skan AI’s process intelligence capabilities discovered adjusters switching to Excel an average of 45 times per case. That kind of operational reality is invisible to process mining, but with observation-based process intelligence, that customer was able to capture it.

Source: Skan AI 7-Dimension Competitive Comparison Framework. Process visibility and time-to-first-insight figures from approved competitive benchmarks.

What data does process intelligence capture in claims workflows?

Process intelligence can help build a digital twin of operations: a real-time, AI-generated replica of how work is actually performed across every system and every team member.

Think of it like a GPS for your claims operation. Traditional tools show you the map. Process intelligence with real-time capture can show you where every car is right now and exactly which roads they are taking.

For insurance claims, this kind of platform captures:

• Application usage: which systems adjusters open, in what order, for how long, and how often they switch between them
• Process variants: every different path a claim takes from intake to closure, including exception routes and manual workarounds
• Time at each stage: actual time spent per step, measured by observed activity rather than system timestamps
• Team-level variation: how different adjusters, teams, or geographic locations handle the same claim type
• Compliance adherence: whether required steps are followed in the correct sequence, in real time, across every case

The result is a continuous, live view of claims operations. Not a snapshot from a survey, not a sample from a time-and-motion study, and not a partial picture drawn from a set of event logs.

What are the core use cases for process intelligence in insurance claims?

Process intelligence for insurance claims is most commonly deployed across four high-impact insurance use cases. Each targets a different lever in the claims operation.

1. Cycle time reduction

Cycle time is the most cited priority in claims operations, but most organizations cannot identify where time is actually being lost. Process intelligence can map every step across the full end-to-end workflow, showing where cases stall, which applications slow adjusters down, and which steps are repeated unnecessarily.

A large US-based healthcare payer working with Skan AI identified $10M in cost savings and reduced processing cycle time by up to 31% per case after uncovering bottlenecks in application switching and manual data re-entry. (Source: Skan AI, PI for Healthcare and Insurance, 2025)

2. Auto-adjudication rate improvement

Higher auto-adjudication rates mean lower cost per claim. Process intelligence reveals which claim types require the most manual intervention and why. It shows the exact steps adjusters take when a claim cannot auto-adjudicate, making it possible to redesign those paths or target automation at the right failure points.

3. Straight-through processing (STP) rate improvement

STP rate measures how often a claim moves from intake to payment without any manual intervention. Process intelligence identifies the exact decision points and system gaps that break the STP path for any claim type.

Operations teams can then prioritize automation investments at those specific failure points, rather than deploying automation broadly across a workflow that is not yet well understood. This sequencing protects return on investment (ROI) on automation spend.

4. Denial root cause analysis

Denial rates are a persistent and expensive problem across carriers. The root causes are often buried deep in operational detail. Process intelligence traces every denied claim backward through the full workflow, exposing which steps, which applications, and which process variants are most closely associated with denial outcomes.

That precision is not available from reporting tools that see only the outcome. It requires observing the process path that led to it.

Why operational visibility cannot wait

DeployingAI in insurance claims without this visibilitycomes with real, measurable risk. Gartner predicts that over 40% of agentic AI projects will be canceled by 2027 due to escalating costs, unclear business value, or inadequate risk controls (Gartner, Inc., Emerging Tech: Avoid Agentic AI Failure: Build Success Using Right Use Cases, 2025).

McKinsey found that only 6% of companies report meaningful EBIT impact from AI investments (McKinsey, “The state of AI in 2025: Agents, innovation, and transformation,” 2025). The gap frequently begins before deployment, when organizations automate workflows they have never properly observed.

That's why it's essential that insurance carriers use process intelligence to close that gap before the investment is made, not after.

Claims process intelligence KPI reference

The table below outlines the core KPIs tracked by process intelligence in insurance claims operations.

Cycle time and rework rate figures from Skan AI Approved Proof Points (Audit Guideline v1.7). Auto-adjudication rate and denial rate benchmarks pending editorial confirmation.

Ready to see how your claims workflow actually runs?

Understanding how your claims operation actually works is the foundation for any meaningful improvement with automation or agentic AI. The next step is applying that visibility to adjudication, where the largest gains in auto-adjudication rate and cycle time are typically found.

Explore Skan AI case studies to see how leading insurance carriers are making that shift.