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 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 in-sample goodness of t tests and out-of-sample 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 |