SERVPRO® Case Study White Paper
How Deeper Claims Analysis Improved Insurance Outcomes
Author: Alan B. Cantor
Founder & Chief Analytics Officer, Risk Analysis Services LLC
Introduction: Turning Claims Data into Negotiating Power
Organizations that insure large, distributed operations often face a familiar challenge:
they bring extensive data to the insurance marketplace, yet the pricing outcomes don’t always reflect their actual loss experience.
This SERVPRO®-related case study shows how Risk Analysis Services (RAS) helped a Risk Retention Group (RRG) insurance organization use deeper claims analytics to challenge assumptions, improve credibility, and reduce insurance costs.
Written by Alan Cantor, a pioneer in applied risk analytics, this paper illustrates how disciplined analysis of historical loss data can materially improve insurance and reinsurance outcomes.
The Client Situation
The client was Restoration Risk Retention Group, a Vermont-domiciled RRG associated with SERVPRO franchises nationwide.
While the organization maintained detailed loss runs and actuarial reports, management believed that:
- Actual loss performance was consistently better than projected
- Traditional actuarial summaries were not fully capturing key drivers of loss
- Insurance and reinsurance pricing did not reflect the organization’s true risk profile
The challenge was not a lack of data — it was translating that data into credible, decision-ready insight.
Analytical Approach
Under the direction of Alan Cantor, Risk Analysis Services applied its RiskMap® methodology to perform a structured, multi-phase analysis of the client’s historical loss data.
The analysis focused on:
- Line-of-coverage performance
- Business segment differences
- Claim frequency and severity patterns
- Long-term loss development trends
Rather than relying solely on aggregate projections, the analysis examined the data from multiple angles to better understand the actual patterns and trends in the empirical data, not just what the totals were.
Key Insights Identified
The RiskMap® analysis revealed that:
- Certain loss projections embedded conservative assumptions that were not supported by historical experience
- Actual claim outcomes showed greater stability than previously reflected in pricing models
- The organization’s retention and reinsurance structure could be optimized using data-based justification
These findings gave management a clearer, more defensible narrative when engaging with their RRG manager and broker, actuaries, reinsurers, regulators, the Board of Directors, and other stakeholders.
Results and Business Impact
Armed with deeper analytics and clearer explanations, the organization was able to:
- Strengthen discussions with reinsurance partners
- Improve confidence in internal retention decisions
- Achieve premium reductions exceeding 15%
More importantly, the organization gained a repeatable analytical framework it could use in future renewals and strategic planning.
As Alan Cantor often emphasizes, credible risk analysis is not about disputing actuarial science — it is about enhancing it with better context and deeper understanding of the data.
Why This Case Study Matters
For risk managers, CFOs, and insurance decision-makers, this case study demonstrates that:
- Historical loss data contains insights often missed by standard summaries
- Deeper and better analytics improve both pricing outcomes and internal confidence
- Insurance negotiations are more productive when supported by clear, well-explained evidence (including full audit trails from historical claims data to details of every step in the analysis)
In short, better questions lead to better analysis — and better outcomes.
About the Author
Alan Cantor is the Founder and Chief Data Analyst of Risk Analysis Services LLC.
He has been applying advanced risk analytics for decades, specializing in achieving superior outcomes for organizations with a history of claims, claims data interpretation, loss modeling, and insurance decision support for complex organizations.
