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 1 - Pure Premium Modeling Using Generalized Linear Models
Authors
Ernesto Schirmacher | Liberty International
Ernesto.Schirmacher@LibertyInternational.com
Chapter Preview
Pricing insurance products is a complex endeavor that requires blending many different perspectives. Historical data must be properly analyzed, socioeconomic trends must be identified, and competitor actions and the company’s own underwriting and claims strategy must be taken into account. Actuaries are well trained to contribute in all these areas and to provide the insights and recommendations necessary for the successful development and implementation of a pricing strategy. In this chapter, we illustrate the creation of one of the fundamental building blocks of a pricing project, namely, pure premiums. We base these pure premiums on generalized linear models of frequency and severity. We illustrate the model building cycle by going through all the phases: data characteristics, exploratory data analysis, oneway and multiway analyses, the fusion of frequency and severity into pure premiums, and validation of the models. The techniques that we illustrate are widely applicable, and we encourage the reader to actively participate via the exercises that are sprinkled throughout the text; after all, data science is not a spectator sport!
| Data | R Code | R Solutions |
| README | Solutions | |
| Raw Data | Data Preparation | |
| Calculations | ||
| Functions |