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 Workersâ€™ Compensation LargeLoss Regression Analysis
 Chapter 10  A Framework for Managing Claim Escalation Using Predictive Modeling
 Chapter 11  Predictive Modeling for UsageBased Auto Insurance
Chapter 4  Frameworks for General Insurance Ratemaking
Beyond the Generalized Linear Model
Authors
Peng Shi  University of WisconsinMadison
pshi@bus.wisc.edu
James Guszcza  Deloitte Consultingâ€™s Actuarial, Risk, and Advanced Analytics
jguszcza@deloitte.com
Chapter Preview
This chapter illustrates the applications of various predictive modeling strategies for determining pure premiums in propertycasualty insurance. Consistent with standard predictive modeling practice, we focus on methodologies capable of harnessing risklevel information in the ratemaking process. The use of such microlevel data yields statistical models capable of making finergrained distinctions between risks, thereby enabling more accurate predictions. This chapter will compare multiple analytical approaches for determining risklevel 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.