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 Large-Loss Regression Analysis
- Chapter 10 - A Framework for Managing Claim Escalation Using Predictive Modeling
- Chapter 11 - Predictive Modeling for Usage-Based Auto Insurance
Chapter 4 - Frameworks for General Insurance Ratemaking Beyond the Generalized Linear Model
Authors
Peng Shi | University of Wisconsin-Madison
pshi@bus.wisc.edu
James Guszcza | Deloitte Consulting’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 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.