Book contents
- Frontmatter
- Contents
- Contributors
- Preface
- Acknowledgments
- 1 Pure Premium Modeling Using Generalized Linear Models
- 2 Applying Generalized Linear Models to Insurance Data: Frequency/Severity versus Pure Premium Modeling
- 3 Generalized Linear Models as Predictive Claim Models
- 4 Frameworks for General Insurance Ratemaking: Beyond the Generalized Linear Model
- 5 Using Multilevel Modeling for Group Health Insurance Ratemaking: A Case Study from the Egyptian Market
- 6 Clustering in General Insurance Pricing
- 7 Application of Two Unsupervised Learning Techniques to Questionable Claims: PRIDIT and Random Forest
- 8 The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
- 9 Finite Mixture Model and Workers’ Compensation Large-Loss Regression Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
- 10 A Framework for Managing Claim Escalation Using Predictive Modeling
- 11 Predictive Modeling for Usage-Based Auto Insurance
- Index
- References
1 - Pure Premium Modeling Using Generalized Linear Models
Published online by Cambridge University Press: 05 August 2016
- Frontmatter
- Contents
- Contributors
- Preface
- Acknowledgments
- 1 Pure Premium Modeling Using Generalized Linear Models
- 2 Applying Generalized Linear Models to Insurance Data: Frequency/Severity versus Pure Premium Modeling
- 3 Generalized Linear Models as Predictive Claim Models
- 4 Frameworks for General Insurance Ratemaking: Beyond the Generalized Linear Model
- 5 Using Multilevel Modeling for Group Health Insurance Ratemaking: A Case Study from the Egyptian Market
- 6 Clustering in General Insurance Pricing
- 7 Application of Two Unsupervised Learning Techniques to Questionable Claims: PRIDIT and Random Forest
- 8 The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
- 9 Finite Mixture Model and Workers’ Compensation Large-Loss Regression Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
- 10 A Framework for Managing Claim Escalation Using Predictive Modeling
- 11 Predictive Modeling for Usage-Based Auto Insurance
- Index
- References
Summary
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, one-way 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!
Introduction
The pricing of insurance products is a complex undertaking and a key determinant of the long-term success of a company. Today's actuaries play a pivotal role in analyzing historical data and interpreting socioeconomic trends to determine actuarially fair price indications.
These price indications form the backbone of the final prices that a company will charge its customers. Final pricing cannot be done by any one group. The final decision must blend many considerations, such as competitor actions, growth strategy, and consumer satisfaction. Therefore, actuaries, underwriters, marketers, distributors, claims adjusters, and company management must come together and collaborate on setting prices. This diverse audience must clearly understand price indications and the implications of various pricing decisions. Actuaries are well positioned to explain and provide the insight necessary for the successful development and implementation of a pricing strategy.
Figure 1.1 shows one possible representation of an overall pricing project. Any one box in the diagram represents a significant portion of the overall project. In the following sections, we concentrate on the lower middle two boxes: “Build many models” and “Diagnose and refine models.”
We concentrate on the first phase of the price indications that will form the key building block for later discussions, namely, the creation of pure premiums based on two generalized linear models.
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- Publisher: Cambridge University PressPrint publication year: 2016
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