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
Preface
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
Summary
In January 1983, the North American actuarial education societies (the Society of Actuaries and the Casualty Actuarial Society) announced that a course based on regression and time series would be part of their basic educational requirements. Since that announcement, a generation of actuaries has been trained in these fundamental applied statistical tools. This two-set volume builds on this training by developing the fundamentals of predictive modeling and providing corresponding applications in actuarial science, risk management, and insurance.
The series is written for practicing actuaries who wish to get a refresher on modern-day data-mining techniques and predictive modeling. Almost all of the international actuarial organizations now require continuing education of their members. Thus, in addition to responding to competitive pressures, actuaries will need materials like these books for their own continuing education. Moreover, it is anticipated that these books could be used for seminars that are held for practicing actuaries who wish to get professional accreditation (known as VEE, or validated by educational experience).
Volume I lays out the foundations of predictive modeling. Beginning with reviews of regression and time series methods, this book provides step-by-step introductions to advanced predictive modeling techniques that are particularly useful in actuarial practice. Readers will gain expertise in several statistical topics, including generalized linear modeling and the analysis of longitudinal, two-part (frequency/severity) and fattailed data. Thus, although the audience is primarily professional actuaries, the book exhibits a “textbook” approach, and so this volume will also be useful for continuing professional development.
An international author team (seven countries, three continents) developed Volume I, published in 2014. You can more learn more about Volume I at
http://research.bus.wisc.edu/PredModelActuaries
Volume II examines applications of predictive models, focusing on property and casualty insurance, primarily through the use of case studies. Case studies provide a learning experience that is closer to real-world actuarial work than can be provided by traditional self-study or lecture/work settings. They can integrate several analysis techniques or, alternatively, can demonstrate that a technique normally used in one practice area could have value in another area. Readers can learn that there is no unique correct answer. Practicing actuaries can be exposed to a variety of techniques in contexts that demonstrates their value.
- Type
- Chapter
- Information
- Predictive Modeling Applications in Actuarial Science , pp. xv - xviiiPublisher: Cambridge University PressPrint publication year: 2016