8 Claims-Processing Performance Metrics for AI in Insurance
Track the Right KPIs to Understand AI Impact & Improve Claims Performance
Insurance executives investing in AI for claims operations need clear evidence that their technology delivers measurable results. Without the right performance metrics, validating investments in AI tools is just guesswork.
While traditional claims-processing key-performance indicators (KPIs) provide useful baseline measurements, they don't capture the full impact of AI implementation and can't help improve performance.
In this article, we'll explore:
- What claims-processing performance metrics measure and why they matter;
- How AI transforms claims operations and improves metrics;
- 8 essential metrics for tracking AI performance in claims-processing; and
- How you benefit from comprehensive AI-performance tracking.
Understanding Claims-Processing Performance Metrics
Claims-processing performance metrics are quantifiable measurements to evaluate the accuracy, effectiveness, and efficiency of claims operations.
Insurance leaders track these KPIs to assess team performance, identify operational bottlenecks, and justify technology investments.
Claims-processing KPIs help demonstrate, among other things:
- How quickly claims move from submission to resolution;
- The volume of claims that adjusters handle daily; and
- What each claim costs to process.
These traditional measurements remain relevant—but claims leaders should expand the metrics they use to understand AI’s sizable impact on claims operations.
Beyond speed and cost, claims leaders must now track data pertinent to accuracy, capabilities, and quality that wasn't as applicable before the rise of AI solutions. The right framework captures both traditional performance indicators and AI-specific value creation.
How AI is Used in Claims-Processing
AI for claims-processing has three main capabilities:
- Intelligent document-processing;
- Evidence-based insights generation; and
- Advanced pattern detection.
Intelligent Document-Processing
Vision-language models understand structured & unstructured data from medical records, employment documentation, legal files, and more to accurately extract relevant information, saving hours of manual labor each day.
Evidence-Based Insights Generation
With generative AI, claims teams can query about complex claims and receive accurate answers with supporting evidence. AI also organizes relevant claims data into clear views, providing out-of-the-box insights with click-through citations to verify information with ease.
Advanced Pattern Detection
Unlike predictive AI models that leverage historical patterns, deterministic AI evaluates claims on their own merits, based on specific claim data and insurance knowledge the AI is trained on, improving transparency, reducing bias, and helping to detect fraud or leakage.
To fully assess AI's impact across these areas (document-processing, insights generation, and pattern detection) and optimize ROI, insurance leaders need a firm handle on claims-processing performance metrics.
8 Essential Claims-Processing Performance Metrics for AI
AI impacts every aspect of claims operations. To validate and optimize AI implementation, below are eight important claims-processing KPIs.
1. Time-to-Decision
What it measures: The duration from claim submission to final determination, encompassing all review, investigation, and approval stages.
Why it matters: Time-to-decision directly impacts customer satisfaction and operational capacity. Faster decisions mean happier claimants and reduced pressure on claims teams, enabling them to handle higher claims volumes.
How AI improves it: AI document automation for insurance can process diverse, dense, and detailed documents in minutes rather than days. Claims teams benefit from immediate, organized, and extracted data. It enables faster, more confident decision-making.
How to measure:
- Track average days from FNOL to claim closure before and after AI implementation.
- Segment by claim complexity to understand where AI delivers maximum impact.
- Monitor the percentage of claims resolved within target timeframes to assess consistency improvements.
2. Document-Turnaround Time
What it measures: How quickly documents, files, images, notes, and more are processed and analyzed from the moment they're added to an existing claim.
Why it matters: Document-processing is a critical bottleneck in claims operations. Processing delays extend claim cycles, frustrating both adjusters and claimants.
How AI improves it: Advanced document-processing with OwlVision analyzes documents, extracts key data, organizes information by category, and links related documents. Claims teams can respond to new information quickly and allocate more time for decision-making.
How to measure:
- Calculate the average processing time for documents from receipt to when actionable insights are available.
- Compare manual processing times against AI-assisted workflows.
- Track the percentage of documents processed within a certain time of receipt to measure real-time processing capability.
3. Accuracy Rate
What it measures: The precision of data extraction and analysis from claims documents, measured by the percentage of correctly processed information versus errors requiring correction.
Why it matters: Accuracy determines decision quality and defensibility. High accuracy rates protect both carriers and claimants while reducing rework and costs.
How AI improves it: Vision-language models trained for insurance documents achieve industry-leading accuracy in data extraction, even from handwritten notes, poor-quality scans, and complex medical terminology. Citations enable adjusters to verify information.
How to measure:
- Track quality-assurance pass rates for AI-processed claims on first review.
- Monitor appeal and dispute rates, as accurate data extraction leads to fewer decisions challenged by claimants.
- Calculate the percentage of requested data fields successfully extracted and populated across claim types.
4. Daily Claim Volume
What it measures: The number of claims each adjuster or team processes daily, indicating operational capacity and productivity levels.
Why it matters: Claim volume directly impacts staffing requirements, operational costs, and service levels. Higher volume per adjuster—without sacrificing quality—means better resource utilization and improved profitability.
How AI improves it: By handling data-intensive tasks, AI empowers adjusters to process more claims without increasing work hours. Claims teams focus on decision-making and claimant interactions, expanding capacity while improving job satisfaction.
How to measure:
- Calculate average claims processed per adjuster before and after AI implementation.
- Segment by claim type and complexity to understand capacity improvements.
- Monitor quality metrics alongside volume to ensure increased throughput retains accuracy.
5. Impacted Claims
What it measures: The volume or proportion of claims where AI-generated insights directly influence outcomes, including claim closures, settlements, overpayment prevention, or fraud detection.
Why it matters: This metric demonstrates AI's tangible business value by quantifying claims where technology directly contributed to better outcomes. It can highlight areas where technology delivers maximum return.
How AI improves it: Owl.co uses a deterministic AI model to find critical information for claims teams, flagging inconsistencies, potential fraud indicators, and claims requiring attention. Proactive intervention prevents costly escalations later.
How to measure:
- Track the number of claims where AI insights led to specific actions, such as: denials based on discovered inconsistencies; settlements adjusted due to new information; number of overpayments identified; fraud investigations initiated; and change in closure rates.
- Calculate the financial impact of these interventions to demonstrate ROI.
6. Daily Resource Reviews
What it measures: The number of specialist reviews or consultations completed daily by nurses, vocational experts, and medical reviewers across the claims portfolio.
Why it matters: Specialist resources represent significant costs and capacity constraints. Ensuring they focus on cases requiring genuine expertise improves both efficiency and claims decisions.
How AI improves it: AI processes claims data, organizing medical records, summarizing treatment histories, etc., before specialist review. OwlAssist makes it easier for specialists to research claims and make queries, ensuring optimal resource utilization.
How to measure:
- Track the number of specialist reviews completed per day before and after AI implementation.
- Monitor average review duration to understand time saved.
- Evaluate reviews where AI-surfaced information directly informed specialist recommendations.
7. Fraud-Detection Rate
What it measures: The percentage of fraudulent claims identified relative to total claims processed, indicating the effectiveness of fraud-prevention strategies.
Why it matters: Insurance fraud costs the U.S. approximately $308 billion annually, with carriers absorbing most losses. Effective fraud detection prevents leakage, stabilizes premiums, and ensures legitimate claimants receive fair treatment.
How AI improves it: AI fraud detection for insurance analyzes claims for discrepancies, inconsistencies, and unusual patterns from submitted information and external data, identifying potential fraud based on factual evidence.
How to measure:
- Calculate the percentage of claims flagged for fraud investigation that prove fraudulent upon review.
- Track dollars saved through early fraud detection.
- Monitor false positive rates to ensure legitimate claims aren't unnecessarily delayed.
8. Cost per Claim
What it measures: The average operational cost to process a single claim from submission to closure, including administrative expenses, labor, and technology.
Why it matters: Cost per claim directly impacts profitability and competitive positioning. Lower processing costs enable better pricing while maintaining margins.
How AI improves it: Automation improves accuracy and provides sophisticated insights, reducing labor hours required per claim. It frees claims teams to focus on creative and meaningful work.
How to measure:
- Calculate total claims-processing costs divided by claims processed before and after AI implementation.
- Break down costs by category (e.g., labor, technology) to identify areas for improvement.
- Track cost trends over time to measure sustained efficiency gains.
Benefits of Tracking Claims-Processing KPIs
AI claims-processing performance metrics have strategic advantages beyond quantitative gains:
- Regulatory Confidence
AI-driven improvements strengthen regulatory compliance with clear audit trails.
- Investment Justification
Clear performance data helps insurance leaders build compelling business cases and demonstrate ROI.
- Competitive Differentiation
Claims leaders who identify trends, address emerging issues, and implement transformative technologies distinguish themselves as industry leaders, attracting talent and building customer trust.
Tracking Claims-Processing KPIs Drives Claims Intelligence
Claims-processing performance metrics for AI provide the evidence insurance leaders need to validate AI investments and grow their organization.
The most successful insurance carriers can combine traditional claims metrics with AI-specific measurements to understand and improve their operations.
This represents Claims Intelligence: your data-driven, precise knowledge working together with powerful AI technology. It enables claims teams to deliver effective, fair, and fast decisions while they enjoy more satisfying work.
Ready to implement AI that delivers measurable improvements across your claims operations?
Book a demo to explore how Owl.co's Claims Intelligence toolkit provides both the performance gains and the accountability you need.