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Parametric Insurance: A New Spin on an Old Product
During this year’s Pinnacle University (Pinnacle U) event, we presented an overview of parametric insurance. Unlike traditional insurance coverage, parametric insurance is not an indemnification product. Instead, it determines a benefit payable in advance of the policy purchase by estimating the loss as accurately as possible, subject to certain conditions being satisfied. The cost of the policy is based on a pre-determined trigger. Examples might include maximum sustained wind speed for hurricane coverage or earthquake magnitude as measured by the Richter scale.
Significant Reductions in Workers’ Compensation Pure Premium Rates for California
Another big California workers’ compensation rate reduction is coming, effective mid-year 2018. This will be the seventh straight decrease, with the last reduction taken January 1, 2018.

This past week, the Workers’ Compensation Insurance Rating Bureau of California (WCIRB) approved and released the pure premium rate filing, effective July 1, 2018. This rate reduction has not yet been approved by the California Department of Insurance, and is open to a public comment period and hearing.

Cost Versus Value
Chris Holt April 03, 2018 Posted in: Blog Posts, Pricing & Product Management
With every product we buy comes an expected cost and a value proposition. Our individual purchasing decisions are always based on more than cost alone.  Hence, “Is the protection plan worth it?” is in the eye of the buyer.
Can the Black-Scholes Model Estimate How Much Premium is Too Much?
A Pinnacle client recently inquired about a paper regarding an approach to estimating the maximum premium appropriate for a captive insurance company. The author sought to determine a ceiling on the premium a company might pay for insurance by treating insurance as a put option and applying a popular financial tool, the Black-Scholes model. 
May I Have Some Neural Networks with My Insurance Data, Please?
Machine learning techniques, particularly Artificial Neural Networks (ANNs), have enjoyed an upsurge in popularity and practical applications in a myriad of disciplines.  The explosion in the variety and volume of available data, coupled with cheap data storage and fast computing power, have placed ANNs front and center in data scientists’ tool boxes. 
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