Predictive Modeling Applications in Actuarial Science

Chapter 5 - Using Multilevel Modeling for Group Health Insurance Ratemaking
A Case Study from the Egyptian Market


Mona S. A. Hammad | Cairo University

Galal A. H. Harby | Al Ahram Canadian University

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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