Predictive Modeling Applications in Actuarial Science
 Volume 1
 Introduction
 Predictive Modeling Foundations
 Predictive Modeling Methods
 Bayesian and Mixed Modeling
 Longitudinal Modeling
 Volume 2
 Generalized Linear Model
 Extensions of the Generalized Linear Model
 Unsupervised Predictive Modeling Methods

Applications on Current Problems in Actuarial Science
 Chapter 8  The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
 Chapter 9  Finite Mixture Model and Workers’ Compensation LargeLoss Regression Analysis
 Chapter 10  A Framework for Managing Claim Escalation Using Predictive Modeling
 Chapter 11  Predictive Modeling for UsageBased Auto Insurance
Chapter 1  Pure Premium Modeling Using Generalized Linear Models
Authors
Ernesto Schirmacher  Liberty International
Ernesto.Schirmacher@LibertyInternational.com
Chapter Preview
Pricing insurance products is a complex endeavor that requires blending many different perspectives. Historical data must be properly analyzed, socioeconomic trends must be identified, and competitor actions and the company’s own underwriting and claims strategy must be taken into account. Actuaries are well trained to contribute in all these areas and to provide the insights and recommendations necessary for the successful development and implementation of a pricing strategy. In this chapter, we illustrate the creation of one of the fundamental building blocks of a pricing project, namely, pure premiums. We base these pure premiums on generalized linear models of frequency and severity. We illustrate the model building cycle by going through all the phases: data characteristics, exploratory data analysis, oneway and multiway analyses, the fusion of frequency and severity into pure premiums, and validation of the models. The techniques that we illustrate are widely applicable, and we encourage the reader to actively participate via the exercises that are sprinkled throughout the text; after all, data science is not a spectator sport!
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