Risk Analysis Services (RAS) helps level the playing field in the insurance marketplace!

SERVPRO® Case Study White Paper

SERVPRO® Case Study White Paper

The following is how Risk Analysis Services helped a recent client benefit from analyses RAS provided.

Many businesses navigate the insurance marketplace annually. Whether shopping for a Business Owners Policy (“BOP”), crafting a commercial package policy, or placing reinsurance for a self-insured entity, the insurance marketplace is an asymmetric battle where the insured shows all and the carriers hold almost all the cards.

The good news is that CFOs, risk managers and owners have improved their companies’ premiums, terms, and coverages by leveraging Risk Analysis Services’ (“RAS”) powerful RiskMap® analyses during annual insurance negotiations.


Below is a testimonial of the bottom-line benefits from RiskMap® analytics:

“RAS assisted our company in documenting loss trends by loss layer, helping us reduce our treaty costs by over 15% by better managing our retention for maximum efficiency. The analysis also helped us support our new retention strategy with regulators, rating agencies, and our Board of Directors.”

Mike Connell, PresidentRestoration Risk Retention Group

For Michael Connell, the President of Restoration Risk Retention Group (a SERVPRO®-related Risk Retention Group domiciled in Vermont), working to place the reinsurance was an annual exercise that he felt was never on a level playing field.  The feeling started each cycle with the reserving actuary’s projected losses for the next year.  Every time the figures were, according to Connell, “consistently overstated when compared to mature accident year results”. Similarly, the actuarial study prepared by our re-insurers overstated actual losses following traditional actuarial practices.

Since the reinsurance market uses their actuary’s projected losses as a component of the pricing, premiums were negatively impacted.  Loss runs should have been enough to paint a compelling picture to lower premiums, but Connell searched for the appropriate tool to better understand true loss trends for use in selecting and justifying the most effective company retentions, and in negotiating more favorable reinsurance terms.

The Casualty Actuarial Society (CAS) published a study “Unstable Loss Development Factors” whose abstract stated:

“actuaries will employ myriad assumptions, judgments, and tools along the way to selecting loss development factors…a recent survey demonstrates variability of selections of loss development factors…and…the resulting reserve projections.”

Fifty-one participating actuaries calculated reserves based on actual claims history provided.  The resulting reserve proposed ranged from $60.2 million to $10.7 million, the mean was $28.0 million, the median was $26.2 million, with a $8.3 million standard deviation and a coefficient of variation 30%. Imagine how low premiums could go if the reserving actuary presented to the lowest number to the insurance carriers.

Connell could feel justified in his belief that the actuary was “over-projecting losses” on the basis of this CAS study alone.

More than premium costs were on Connell’s mind.

When he discussed his needs with Alan B. Cantor, co-founder of Risk Analysis Services LLC (RAS), Connell asked:

“How do we create useful information from the tons and tons of data?”  

“Could RAS help formulate a “predictive risk score” so the pricing of the insurance to the franchisees would better reflect their individual and specific businesses?”   

“Could RAS suggest various “layers” that could inform thinking about capitalization of the RRRG itself?”  

The goal was to start the process to get better pricing in the insurance market; then, continue to use the insurance mechanism to create more efficiencies in standard operating procedures and to improve the customer experience.  Ultimately, an initial consulting contract called for three phases of analysis by Risk Analysis Services:

       First: review insurance coverages by line of protection
       Second: study lines of business by customer type
       Third: evaluate claims experience by peril insured against

RAS created an artificial intelligence engine called RiskMap® into which Connell’s RRRG loss-run data and other information fed sophisticated analytical algorithms. During the initial review of the output, Connell said RiskMap® “aligned with our analyses and projections” that could be used accurately select and justify his “picture of what we would need to book” for incurred losses.  

Not only would this improve cash-flow, but it would also improve the RRRG financial performance by eliminating erratic swings in calendar year results resulting from booking excess losses that were then released in the annual reserve study.  

RAS found “the stratification layers” sought by Connell; upper levels could potentially create other reinsurance options, to increase competition in the reinsurance marketplace for Restoration RRG.

These detailed proprietary analytics also creates a scorecard to compare the results of tests on operational strategies.  The analysis clearly demonstrated the success of the Company’s improved litigation management practices in reducing and managing large and complex claims.

Below is a “noteworthy” graphic to Connell.  It validated his belief that projected losses were always too high in one coverage because actual claims never intersected the projected claims.  Furthermore, it illustrated that certain business operating statistics did not relate to claims and should not be used as the exposure units for pricing the insurance.

Based on RAS’s analyses, Restoration RRG’s premiums were reduced by over 15%.  

In the hands of a knowledgeable broker, RiskMap® analytics helps level the playing field in the insurance market.