General Iteration Algorithms for Classification Ratemaking

In this study, we propose a flexible and comprehensive iteration algorithm called “general iteration algorithm” (GIA) to model insurance ratemaking data. The iteration algorithm is a generalization of a decades-old iteration approach known as “minimum bias models.” We will demonstrate how to use GIA to solve all the multiplicative minimum bias models published to date and the commonly used multiplicative generalized linear models (GLMs), such as gamma, Poisson, normal, and inverse Gaussian models. In addition, we will demonstrate how to apply GIA to solve the broad range of GLM models, mixed additive and multiplicative models, and constraint-optimization problems that pricing actuaries often deal with in their practical work.