The second meeting of the Warranty Administration Xchange Group built directly on the momentum from the initial session—shifting the conversation from identifying challenges to actively benchmarking how organizations are managing manual claim assessment, automation, and operational efficiency.
With participation from OEMs including Volvo, Subaru, GM, Bridgestone, Kia, Cummins, VW, Toyota and Winnebago, the discussion reflected a wide spectrum of maturity levels—but also a clear convergence around where warranty administration is heading next.
“We all have limited people, limited time, and a growing need to decide which claims really deserve a human review.”
Manual Claim Assessment: Still Necessary, But Under Pressure
One of the most revealing discussions centered on how much of the warranty process still relies on manual review.
Across the group manual claim assessment ranged from 4% to some organizations still operating at 100% manual review due to inspection requirements.
Despite this variability, there was broad agreement on one point:
Manual review is not going away—but it must become more targeted.
Common triggers for manual review included:
- Major components (engines, transmissions)
- Excessive or atypical labor time (A-time, diagnostic thresholds)
- Third-party labor time claims (often requiring deeper validation)
- Specific rule-based flags or approval codes
Balancing The Right Amount of Manual Assessing
Not every business can jump straight to heavy automation. One OEM shared an important caution while moving from 100% manual toward a target of 80% auto-approval (meaning 20% still manually reviewed). Their concern wasn’t just speed. It was visibility across the product lifecycle. If claims are also your early warning system, pushing automation too far can reduce the fidelity of what you learn, and that can hurt you long-term.
The Real Opportunity: Reducing Low-Value Work
A recurring theme was the cost of reviewing claims that do not justify the effort.
Participants discussed how low-dollar claims often consume disproportionate resources, while higher-risk cases may deserve greater focus.
Leading strategies included:
- Setting minimum claim-value thresholds
- Removing unnecessary routing rules
- Using historical approval rates to streamline low-risk categories
Best Practice: One team shared that they reduced approval routing codes from 43 to 10 after evaluating impact and effectiveness—freeing resources for more meaningful review.
From Rules to Intelligence: Emerging Automation Strategies
The group is actively transitioning from static rules toward more dynamic, data-driven approaches.
1. Risk-Based Scoring Models
Organizations are increasingly moving beyond fixed rule sets toward data-driven decision models.
Emerging approaches include:
- Risk-based scoring to prioritize claims by likelihood of concern
- Pattern recognition to flag deviations from normal behavior
- Dynamic thresholds that adjust based on workload or strategy
Goal: Rather than reviewing everything, the goal is to focus attention where it matters most. Identify claims that don’t look like the rest, instead of reviewing everything.
2. Third-Party Labor Time Remains A Friction Point
Third-party labor time claims (especially in states like New Jersey, New York, Illinois, Minnesota) continue to create operational strain.
Challenges include:
- Heavy manual review requirements
- Limited integration with external labor guide providers
- Growing claim backlogs
- A foundation for future automation
Best practice: Capture both the OEM labor time and the third-party labor time description in the claim. That creates history. With enough paid-claim history, build your own internal reference table showing minimum, maximum, average, mean, and delta by labor operation. Reviewers then use that “goal post” data to move faster: if a claim sits inside expected ranges, it needs less attention; if it’s outside, that’s where deeper review begins.
This creates faster adjudication today and a stronger foundation for automation tomorrow.
Dealer Performance as a Process Lever
Another key theme: Not all claims—or dealers—should be treated equally.
Several organizations are tying claim workflows more directly to dealer performance.
Examples included:
- Dealer scorecards for submission quality and accuracy
- Tiered approval models based on dealer behavior
- Recertification requirements for underperforming dealers
High-performing dealers gain more autonomy, while lower-performing dealers receive additional oversight.
The result is a more balanced system that rewards quality and reduces repeated process inefficiencies.
SLAs and Throughput: Balancing Speed vs. Value
Performance metrics varied widely, but common benchmarks included:
- 50–150 claims per assessor per day / 80% handled within 7 days
- Targets of 24-hour response times for initial claim handling
- Focus on:
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- Claim aging
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- Throughput efficiency
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- Review quality (not just volume)
Critical insight: Reviewing fewer, higher-value claims can deliver greater ROI than processing high volumes with low impact.
System Constraints Still Matter
Despite progress in analytics and automation, legacy systems continue to limit efficiency:
- Slow claim processing interfaces
- Fragmented dealer management system (DMS) integrations
- Workarounds outside core systems becoming the norm
This reinforces a recurring challenge: Technology strategy must evolve alongside process improvements.
AI Potential Is Clear – Execution Still Lags
There was no shortage of ideas for AI applications:
- Automated claim scoring
- Pattern recognition for anomaly detection
- Intelligent routing and prioritization
- Labor time validation and benchmarking
But adoption barriers remain:
- Data-sharing concerns
- Internal approval hurdles
- Difficulty operationalizing concepts into production systems
The opportunity is significant—but operational readiness remains the deciding factor.
Looking Ahead: From Claims Processing to Anomaly Detection
The group aligned on a key next step: Shifting focus toward identifying and managing unsubstantiated or anomalous claims.
Future discussions will explore:
- Common patterns (e.g., mileage inconsistencies, duplicate work, VIN mismatches)
- Detection methods (rules, analytics, AI)
- Appropriate responses (rejection, audit, corrective actions)
What This Means for Warranty Leaders
The direction is becoming clear across the industry:
- Manual review will remain—but must be smarter
- Automation will expand—but needs better data foundations
- Dealer behavior will increasingly drive process design
- AI will play a role—but only where organizations can operationalize it
Most importantly: The goal is no longer just processing claims—it’s prioritizing the right claims.
Continuing the Conversation
The Warranty Administration Xchange Group will continue building on these discussions, with the next session focused on anomaly detection and claim integrity scheduled for August 13 at 3:00pm ET.
Additional MAPconnected sessions—particularly upcoming AI case studies—will further explore how these concepts are being applied in real-world warranty environments.
As organizations work to modernize claims operations, peer benchmarking and shared best practices remain essential to building smarter, more resilient warranty processes.
Register Now For Next Meetup August 13
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