Predictive Modeling Applications in Actuarial Science

doi:10.1017/CBO9781139342681

Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes. Forecasting financial events is a core skill that actuaries routinely apply in insurance and other risk-management applications. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used to gain a competitive advantage in situations with complex data. Volume 2 examines applications of predictive modeling. Where Volume 1 developed the foundations of predictive modeling, Volume 2 explores practical uses for techniques, focusing on property and casualty insurance. Readers are exposed to a variety of techniques in concrete, real-life contexts that demonstrate their value and the overall value of predictive modeling, for seasoned practicing analysts as well as those just starting out.

Winner, 2018 Kulp-Wright Book Award, American Risk and Insurance Association

Praise for Volume 1:‘With contributions coming from a wide variety of researchers, professors, and actuaries - including several CAS Fellows - it's clear that this book will be valuable for any actuary whose main concern is using predictive modeling in his or her own work.'

David Zornek Source: Actuarial Review

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Contents

Frontmatter
pp i-vi
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Contents
pp vii-x
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Contributors
pp xi-xiv
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Preface
pp xv-xviii
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Acknowledgments
pp xix-xx
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1 - Pure Premium Modeling Using Generalized Linear Models
pp 1-38
- By Ernesto Schirmacher, Liberty International, part of Liberty Mutual Insurance
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2 - Applying Generalized Linear Models to Insurance Data: Frequency/Severity versus Pure Premium Modeling
pp 39-59
- By Dan Tevet, Liberty Mutual Insurance in Boston
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3 - Generalized Linear Models as Predictive Claim Models
pp 60-99
- By Greg Taylor, University of New SouthWales, James Sullivan, University of New South Wales
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4 - Frameworks for General Insurance Ratemaking: Beyond the Generalized Linear Model
pp 100-125
- By Peng Shi, University of Wisconsin–Madison, James Guszcza, University of Wisconsin–Madison
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5 - Using Multilevel Modeling for Group Health Insurance Ratemaking: A Case Study from the Egyptian Market
pp 126-158
- By Mona S. A. Hammad, Cairo University, Egypt, Galal A. H. Harby, University in Egypt
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6 - Clustering in General Insurance Pricing
pp 159-179
- By Ji Yao, University of Kent
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7 - Application of Two Unsupervised Learning Techniques to Questionable Claims: PRIDIT and Random Forest
pp 180-207
- By Louise A. Francis, Casualty Actuarial Society (CAS)
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8 - The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
pp 208-223
- By Glenn Meyers, Oakland University
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9 - Finite Mixture Model and Workers’ Compensation Large-Loss Regression Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
pp 224-260
- By Luyang Fu, University of Illinois at Urbana-Champaign
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10 - A Framework for Managing Claim Escalation Using Predictive Modeling
pp 261-289
- By Mohamad A. Hindawi, Allstate Insurance Company, Claudine H. Modlin, Bradley University
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11 - Predictive Modeling for Usage-Based Auto Insurance
pp 290-308
- By Udi Makov, University of Haifa, Jim Weiss, Chartered Property Casualty Underwriter
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Index
pp 309-315
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Predictive Modeling Applications in Actuarial Science

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Contents

Frontmatter
pp i-vi

Contents
pp vii-x

Contributors
pp xi-xiv

Preface
pp xv-xviii

Acknowledgments
pp xix-xx

1 - Pure Premium Modeling Using Generalized Linear Models
pp 1-38

2 - Applying Generalized Linear Models to Insurance Data: Frequency/Severity versus Pure Premium Modeling
pp 39-59

3 - Generalized Linear Models as Predictive Claim Models
pp 60-99

4 - Frameworks for General Insurance Ratemaking: Beyond the Generalized Linear Model
pp 100-125

5 - Using Multilevel Modeling for Group Health Insurance Ratemaking: A Case Study from the Egyptian Market
pp 126-158

6 - Clustering in General Insurance Pricing
pp 159-179

7 - Application of Two Unsupervised Learning Techniques to Questionable Claims: PRIDIT and Random Forest
pp 180-207

8 - The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
pp 208-223

9 - Finite Mixture Model and Workers’ Compensation Large-Loss Regression Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
pp 224-260

10 - A Framework for Managing Claim Escalation Using Predictive Modeling
pp 261-289

11 - Predictive Modeling for Usage-Based Auto Insurance
pp 290-308

Index
pp 309-315

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Predictive Modeling Applications in Actuarial Science

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