How Top Insurers Convert Claims Data Into Automation ROI
Insurance carriers generate vast amounts of claims data every day, yet it records what happened in backend systems rather than how claims work was done. Measuring efficiency gains depends on complete visibility into how that work happens in practice, not how it is designed to happen.
Traditional tools leave claims processes only partly visible. They miss the manual steps, workarounds, and application-switching that consume processing time and add operational cost.
This gap is where automation programs stall. Desktop-level observation built on privacy by design records how work moves across applications, including manual steps that backend logs miss. Carriers use that baseline to rank automation opportunities by expected return on investment (ROI), locate handoffs and rework, and execute approved work under defined procedures across claims, underwriting, and customer service operations.
Why do insurance claims operations carry hidden automation potential?
Insurance claims workflows are document-heavy, high-volume, and rule-driven, which makes them strong candidates for automation, but that potential stays hidden when the full picture of how work happens is not captured.
Event logs show what changed in systems. They do not show what employees did to make those changes happen. An adjuster who switches to Excel 45 times per claim, copies data between 3 applications, or manually re-enters information already captured elsewhere does not appear in any event log.
Employees spend almost 10% of their time opening, closing, and switching between technology applications (Gartner, 2022). Adjusters also copy data from one system to another and re-enter information that already exists elsewhere. In a large insurer’s claims operation, this time adds up across every team and every region.
Most carriers have lacked the visibility to find that potential, which is the gap process intelligence for insurance addresses.
Where does manual work collect in the claims lifecycle?
Manual work collects at the handoffs between lifecycle stages, where claims data moves between applications and where exceptions leave the automated path for an adjuster.
- First notice of loss (FNOL): staff can re-key FNOL data from calls, emails, and portals into the claims system.
- Intake: staff can match incoming claim documents to the right file.
- Classification: staff can sort each claim by type and line of business.
- Validation: staff can review claim documents against policy details and coverage.
- Triage: supervisors can assign files to handlers.
- Adjudication: adjusters can review evidence and decide coverage and amount.
- Exception handling: adjusters can resolve exceptions that fail a rule.
- Payment or settlement: staff can approve payments and reconcile them across systems.
Claims that pass every rule move straight through without a handler, which is straight-through processing (STP). Claims that fail a rule or exceed an authority threshold route to an adjuster for review. McKinsey notes that full STP for simple claims “already exists” (McKinsey, 2022).
Fraud scoring, which rates how likely each claim is to be fraudulent, sorts files at triage (the first review that decides who handles a file) into three bands:
- Above the referral threshold: The file goes to the special investigation unit (SIU) before payment.
- In the band beneath it: The adjuster reviews the flags and decides whether to refer.
- Below the band: The claim proceeds.
Leakage is what carriers pay beyond what the contract owes. Bain defines it as “the difference between what is paid vs. what is owed per the contract” and estimates that generative AI could reduce leakage by 30-50% (Bain, 2024).
What makes traditional process discovery ineffective for insurance claims?
Traditional process discovery can miss the manual work where automation opportunities sit, because process mining relies on event logs from connected systems. Those logs show what happened in databases, not what employees did to make it happen.
Manual observation and employee interviews are the alternative, but they are limited by human recall. Employees adapt to inefficiencies and stop noticing them. An observer can only capture what occurs during the observation window, not the full range of process variations that occur across different users, regions, and claim types.
Automation programs can then target processes that look broken on paper rather than the processes that cost the most time and money. Investments can go to the wrong processes, so programs risk missing ROI projections and stalling before they reach their targets.
The problem is compounded by three constraints (Deloitte, 2020):
- Legacy core systems, where mainframe (a large central computer that runs core records), virtual desktop infrastructure (VDI), and core claims platforms make event-log extraction an integration project, and event-log approaches reach end-to-end depth only after integrating every system of record (the system that holds the official data for a process) while still missing work that never reaches a log.
- Data quality, because event logs are by-products of transactions and can be incomplete.
- Change management (guiding people through new ways of working), because new intake or routing steps change how adjusters work, so programs need a baseline to measure adoption against. In Deloitte’s 2020 intelligent automation survey of 441 executives, only 38% reported mature process definitions.
Desktop-level process visibility closes that gap by capturing process activity across the applications a claim passes through, then using that data to surface automation opportunities ranked by expected impact.
What are the highest-impact automation plays for insurance claims?
Insurance claims operations have four main automation plays aimed at manual work:
- Intelligent document processing: Software that extracts fields from claim documents
- Automated claims routing: Assigning claims to handlers by complexity and type
- Customer touchpoint optimization: Removing redundant information collection across service channels
- Workflow consolidation: Merging or automating sequential workflows
The table below describes each play.
Each play relies on a distinct set of enabling technologies. Optical character recognition (OCR) and intelligent document processing extract fields from claim documents, while natural language processing (NLP) reads unstructured adjuster notes and correspondence, and machine learning models score claim complexity for routing. Robotic process automation (RPA) moves data between systems on fixed rules, artificial intelligence (AI) handles steps that need context or judgment, and computer vision reads screen content where applications expose no interface.
| Play | What it targets | Typical impact |
|---|---|---|
| 1. Intelligent document processing | Claims intake, underwriting document review, manual data extraction | Shorter turnaround on intake and document review; improved extraction accuracy |
| 2. Automated claims routing | Manual assignment of claims to handlers based on complexity and type | More consistent claim assignment across handlers |
| 3. Customer touchpoint optimization | Redundant information collection across service channels | Lower handling cost from removing redundant data entry |
| 4. Workflow consolidation | Sequential workflows that can be merged or automated | Scales with claim volume; immediate cost reduction |
Programs can execute these plays in sequence, using process intelligence data (see the Insurance Claims Automation Playbook) to choose the combination that fits their specific claim mix and operating model.
What ROI have insurers achieved through claims automation?
Skan AI deployments at a Fortune 200 P&C carrier, an F500 insurer and a leading healthcare payer show measurable productivity and cost outcomes.
- Fortune 200 P&C carrier: 14% productivity gain
- F500 insurer: $6M in savings for a quick claims process
- Leading healthcare payer: $15M in operational savings
Three in five top US insurers use Skan AI, along with Capita, Dell Technologies, Infoniqa, Protective, Unum, and Definiti.
“Before, it was turn on AI and hope. Now we know exactly which processes to automate, what we’ll save, and how to prove we got there.” Jennifer Redden, Head of Technology, Definiti
Executing targeted automation plays starts from a complete, observed picture of how work happens, which shows which opportunities rank highest by expected ROI.
How does Skan AI identify automation opportunities across insurance operations?
Skan AI identifies automation opportunities by observing claims, underwriting, and customer service work at the desktop level, building privacy by design into the observation, and ranking opportunities by expected ROI.
Skan AI Blueprint, Skan AI Process Intelligence and Skan AI Agents run on one Context Graph of Work.
- Blueprint helps insurance enterprises understand where process inefficiencies happen across the organization.
- Process Intelligence observes in depth the claims, underwriting, and customer service work Blueprint identifies and uses that observation to build the Context Graph of Work, a map of how work moves across applications and steps that works as a Digital Twin of Operations, grounded in the observed record of how the work is done.
- Agents execute approved work under Agentic Operating Procedures (AOPs), the procedures that set escalation thresholds and stopping conditions.
Unlike process mining tools that analyze event logs, Skan AI pairs end-to-end process depth with zero integrations, APIs or logs. The Skan Virtual Assistant (Skan VA), a lightweight desktop sensor, captures the manual steps that do not appear in backend logs so the result reflects how work happens in practice.
For insurance operations leaders, this means visibility into:
- Where claims handlers switch between applications and how many times per case
- Which process variations create inconsistency across regions and teams
- Where compliance gaps exist in how escalation procedures are followed
- Which automation opportunities will generate the highest return
That means Skan AI can capture work across Guidewire, Duck Creek, ACORD workflows, and mainframe or VDI claims systems without requiring application programming interface (API) integration or backend connectors. Proving ROI does not depend on an IT integration project, although some IT participation may still be needed for deployment.
What is the first step toward measurable claims automation savings?
The first step is establishing a complete baseline view of how claims operations run today. Automation programs that skip this step can target the wrong processes and miss their ROI projections.
The first step follows the sequence of Blueprint, Process Intelligence and Agents.
- Blueprint ranks automation opportunities by ROI into a dollar-weighted roadmap built on observed data, not assumptions, so teams address the highest-impact areas first.
- Process Intelligence identifies non-value-added activities, benchmarks performance across regions, and shows where work meets the most unnecessary handoffs and rework. It also uses process variants and application usage patterns to reveal redundancies.
- Agents execute approved work under AOPs.
The critical question for insurance operations leaders is not whether automation opportunities exist in their claims workflows but whether those opportunities get identified and captured.
Request a demo of Skan AI focused on your claims operation to see how it identifies claims automation ROI in your environment. Score other options against your own security and ROI requirements with the Buyers Guide.
Frequently Asked Questions
Insurance claims automation is the use of technology to reduce or eliminate manual tasks in claims processing, including document intake, data validation, eligibility verification, and claim routing. Process intelligence identifies which tasks are highest-priority for automation by observing how work actually happens across claims teams.
Process intelligence for insurance claims is a technology approach that combines computer vision, AI, and advanced analytics to observe and analyze how claims workflows operate in practice, using desktop-level observation built on privacy by design. It captures application interactions and process variations across the workforce, giving operations leaders data to decide what to optimize and automate.
Traditional process mining analyzes event logs from backend systems and shows what happened in databases. Process intelligence observes the human-computer interaction layer through desktop-level observation built on privacy by design, including desktop activity across applications. This means it captures manual workarounds, cross-system data copying, and workflow variations that event logs do not record.
Claims processing, underwriting, and contact center operations are strong candidates for process intelligence. These areas involve high transaction volumes, distributed teams using multiple applications, and cases that span days to weeks, which creates conditions where process intelligence can identify significant automation opportunities.
With Skan AI, initial results can begin in weeks once observation is deployed and a baseline of how work runs is captured. Skan AI’s zero-integration design allows deployment to begin without backend connectors, and the platform surfaces automation opportunities ranked by expected impact so teams can prioritize plays by return from the start.
Skan VA captures cross-application work through desktop-level observation built on privacy by design, without backend connectors, so you do not need Guidewire or Duck Creek event logs to start. Core claims system logs record only changes made inside that system, not claims work done in spreadsheets, office tools, and other applications, so the baseline does not wait on a log-extraction project, although some IT participation may still be needed for deployment.
Insurers run claims on core claims platforms such as Guidewire and Duck Creek, often alongside mainframe or virtual desktop infrastructure (VDI) claims systems. They also use ACORD forms, spreadsheets, and document tools, with robotic process automation (RPA), intelligent document processing, and fraud scoring tools layered on top. A single claim often crosses several of these applications.
Both, at different steps: AI executes approved, rule-based steps within defined authority, and adjusters decide where judgment, authority limits, or risk thresholds require it. On an agentic AI platform, Skan AI Agents follow AOPs that set escalation thresholds and stopping conditions. A control layer records inputs, outputs, actions, decisions, and rationales for case-level review.
Skan PRISM, an on-premises privacy and information security gateway, applies pixel-level redaction inside the customer environment before protected information can leave, so raw screen data stays within the customer-controlled perimeter under privacy by design. Skan AI does not capture email content or keystrokes; it records process patterns and workflow data.
Skan AI holds SOC 2 Type 2, ISO 27001, and ISO 42001 certifications, and is HIPAA, GDPR, and CCPA compliant. Compliance teams should still verify the deployment against the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), and state rules using Skan AI’s current assurance documentation.
