Predictive Modeling Applications in Actuarial Science

Chapter 4 - Frameworks for General Insurance Ratemaking
Beyond the Generalized Linear Model


Peng Shi | University of Wisconsin-Madison

James Guszcza | Deloitte Consulting’s Actuarial, Risk, and Advanced Analytics

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This chapter illustrates the applications of various predictive modeling strategies for determining pure premiums in property-casualty insurance. Consistent with standard predictive modeling practice, we focus on methodologies capable of harnessing risk-level information in the ratemaking process. The use of such micro-level data yields statistical models capable of making finer-grained distinctions between risks, thereby enabling more accurate predictions. This chapter will compare multiple analytical approaches for determining risk-level pure premium. A database of personal automobile risks will be used to illustrate the various approaches. A distinctive feature of our approach is the comparison of two broad classes of modeling frameworks: univariate and multivariate. The univariate approach, most commonly used in industry, specifies a separate model for each outcome variable. The multivariate approach specifies a single model for a vector of outcome variables. Comparing the performance of different models reveals that there is no unique solution, and each approach has its own strengths and weaknesses.