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

Risk Analysis Services (RAS) RiskMap® Competitive Advantage


Risk Analysis Services (RAS) RiskMap® Competitive Advantage

At Least 4 years of loss data with 15 or more claims per year (5 to 10 years of data preferred; works for up to 14 years) .

RiskMap® automates the process of running dozens or hundreds of Loss Development and Loss Forecasting Regression analyses … in minutes or seconds… in a manner which produces attractive Detailed Analyses, Executive Summary analyses, Comparative Analyses to enable effective understanding and insights of otherwise hidden patterns in the data.

We run the Loss Forecasting Regressions for 3-10 SIRs to credibly estimate Expected Losses at successive Loss Layers to Arbitrage the Gain where Additional Expected Losses are Less Than the Premium Credit offered by the insurance market.

For example, where a client’s estimated expected additional cost (based on RAS analysis) is $100k for increasing retention from $250 to $500k, and the insurance market (based on its actuarial estimates including benchmarking – vs. ours based purely on client’s actual losses) assumes $900k for that layer, and offers a $400k credit, then our client has gained $300k savings.  

Conventional actuarial analysis with benchmarking incorporates industry loss and exposure data.  Where a client’s proactive safety and loss control and mitigation strategies enable it to perform better than its peers in its industry and exposures, then they may be poorly treated by the insurance market.  We have been able to successfully correct that.

What we do is like going to Vegas where we can see all the cards in the deck – which is illegal in Vegas, but perfectly legal in the insurance industry.

The system does not replace any actuarial process. 

The system augments the processes of Loss Development analysis and bi-variate frequency and severity regression (loss forecasting) analyses.

The system credibly maps estimated expected losses by successive loss layers.

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