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 5 
Using Multilevel Modeling for Group Health Insurance Ratemaking
A Case Study from the Egyptian Market
Authors
Mona S. A. Hammad  Cairo University
mona_hammad@hotmail.com
Galal A. H. Harby  Al Ahram Canadian University
Chapter Preview
As explained in more detail in Volume I of this book, multi level modeling represents a powerful tool that recently gained popularity in actuarial research. It builds on recent findings linking credibility theory in actuarial science to the linear mixed model in statistics. In this chapter, we present a practical application of multilevel modeling in dealing with the complex nature of group health insurance policies within a ratemaking context. In particular, using a real dataset from one of the major insurance companies in Egypt, we illustrate how the pure premiums for these policies can be estimated using both these advanced models and traditional (single level) general linear models. The results are compared using both insample goodness of t tests and outofsample validation.
The overall aim is to illustrate the additional advantages gained by using these advanced types of models, more specifically, its ability to allow for the complex data structures underlying group health insurance policies. These include, for example, multidimensional benefit packages and panel/longitudinal aspects, which are often necessary for experience rating purposes.
Interested readers may refer to Chapters 2, 7, 8, and 9 in Volume I of this book for more detail regarding the models used in this chapter.
Data  R Code  SPSS Code 
Data  Single Level Models  Single Level Models 
Multilevel Model  Multilevel Model 