Insurance companies gain competitive advantage by offering better rates and services to attract and retain the best customers. Generalized linear models (GLMs) have become popular and proven techniques for ratemaking and actuarial work over the past decade. Claim frequency is typically modeled using a Poisson distribution; severity is modeled using a gamma distribution; and pure premium is modeled using a Tweedie distribution. Insurance providers use these models to accurately estimate losses and set the most competitive rates accordingly. This paper ties the theory of ratemaking using GLMs to case studies that use real insurance data and shows you how to use SAS ® Enterprise Miner ™ to model claim frequency, severity, and pure premiums
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