| Title | Predictive Modeling Applications in Actuarial Science, Volume 2: Case Studies in Insurance |
| Author(s) | Edward W. Frees, Glenn Meyers, Richard A. Derrig |
| Publisher | Cambridge University Press |
| Publication Year | 2016 |
| Edition | 1st Edition |
| Language | English |
| Pages | 330 pages |
| ISBN | 9781107133796 |
| Genre / Domain | Actuarial Science, Statistics, Finance, Insurance |
| File Size | 8.81 MB |
| Extension |
Book Summary
"Predictive Modeling Applications in Actuarial Science, Volume 2: Case Studies in Insurance" is an essential resource for actuaries and insurance professionals seeking to understand the practical implementation of predictive modeling techniques. Published by Cambridge University Press in 2016, this volume is edited by three distinguished experts in the field: Edward W. Frees, Glenn Meyers, and Richard A. Derrig. It provides a comprehensive collection of case studies that demonstrate how statistical and machine learning models are applied to solve real-world problems in the insurance industry.
The book is structured around a diverse set of insurance applications, ranging from pricing and underwriting to risk management and fraud detection. Each chapter is a self-contained case study that carefully outlines the business problem, the data used, the modeling approach, and the practical results. The authors explore a wide variety of predictive modeling techniques, including generalized linear models, survival analysis, machine learning algorithms, and text mining. The emphasis is always on the practical application and the actionable insights that can be derived from the models.
A key strength of this volume is its commitment to using real data and providing detailed guidance on implementation. The case studies are not hypothetical exercises; they are drawn from actual insurance data and demonstrate the challenges and nuances of working with messy, real-world data. The book also includes discussions on how to communicate model results to stakeholders and how to integrate predictive models into the decision-making processes of an insurance company. This makes it an invaluable tool for practitioners who need to bridge the gap between theory and practice.
This book is primarily aimed at practicing actuaries, data scientists, and risk managers working in the insurance industry. It is also an excellent resource for students in actuarial science, statistics, and finance who are looking to apply their theoretical knowledge to real-world problems. The level of the content is advanced, assuming a solid foundation in statistical modeling and actuarial principles. The case studies provide a deep dive into specific applications, making it more of a reference for professionals rather than an introductory textbook.
The reputation of the editors and the publisher ensures the high quality and relevance of the content. Edward W. Frees is a leading authority on actuarial statistics, and his co-editors are equally respected figures in the field. The collection of case studies covers a broad spectrum of insurance domains, including auto insurance, property and casualty insurance, and health insurance, making it a valuable resource for professionals across the industry. This volume is an indispensable addition to the library of anyone serious about applying predictive analytics in insurance.
Key Features
- Presents a collection of real-world case studies demonstrating predictive modeling applications in insurance.
- Covers a wide range of topics including pricing, risk management, fraud detection, and claims modeling.
- Features contributions from leading experts and practitioners in the field of actuarial science.
- Provides detailed explanations of modeling techniques and their practical implementation.
- Uses real insurance data to illustrate the challenges and nuances of predictive analytics.
- Includes discussions on model validation, communication, and integration into business processes.
- Offers a comprehensive overview of statistical and machine learning methods used in insurance.
- Ideal for practicing actuaries, data scientists, and risk managers.
- Serves as a valuable reference for students in actuarial science and related fields.
- Published by Cambridge University Press, ensuring high academic and professional standards.
About the Authors
Edward W. Frees is the Hickman-Larson Professor of Actuarial Science at the University of Wisconsin-Madison. He is a leading researcher in actuarial statistics and predictive modeling, with numerous publications in top actuarial and statistical journals. He is also the author of several influential books on regression modeling and actuarial science.
Glenn Meyers is a consulting actuary with extensive experience in the property-casualty insurance industry. He has held senior positions at major insurance companies and is known for his work on reserving, pricing, and data analytics. He is a Fellow of the Casualty Actuarial Society (CAS) and has served on numerous committees.
Richard A. Derrig is the President of OPAL Consulting, LLC, and a former Vice President of Research at the Insurance Fraud Bureau of Massachusetts. He has published extensively on fraud detection, predictive modeling, and actuarial science. He is a Fellow of the CAS and has been a professor at Boston University.
Related Books
- Predictive Modeling Applications in Actuarial Science, Volume 1: Predictive Modeling Techniques — Edward W. Frees, Glenn Meyers, Richard A. Derrig
- Loss Models: From Data to Decisions — Stuart A. Klugman, Harry H. Panjer, Gordon E. Willmot
- Foundations of Predictive Analytics — James Wu, Stephen Coggeshall
- Actuarial Models: The Mathematics of Insurance — Vladimir I. Rotar
- Statistical Modeling and Machine Learning for Actuarial Science — Jennifer L. Hu, Robert C. P. Wong
- Non-Life Insurance: An Introduction to Actuarial Techniques — Michel Denuit, Donatien Hainaut
- Risk Theory and Actuarial Mathematics — R. Kaas, M. Goovaerts, J. Dhaene, M. Denuit
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Frequently Asked Questions (FAQ)
Q : What is the difference between Volume 1 and Volume 2 of this series ?
R : Volume 1 focuses on introducing and explaining the fundamental techniques and methods of predictive modeling. Volume 2, on the other hand, is dedicated entirely to case studies, showing how these techniques are applied in real-world insurance scenarios across various domains.
Q : Do I need to read Volume 1 before reading Volume 2 ?
R : While it is helpful to have a solid understanding of the techniques covered in Volume 1, Volume 2 can be used independently. It is designed for professionals who already have a background in predictive modeling and are looking for practical applications.
Q : What types of insurance applications are covered in the case studies ?
R : The book includes case studies on pricing, underwriting, risk management, fraud detection, claims modeling, and reserving, covering areas like auto, property, casualty, and health insurance.
Q : What software or tools are used in the case studies ?
R : The case studies typically use widely available statistical software such as R and SAS, with code and examples provided to facilitate replication and adaptation by practitioners.
Q : Is the book suitable for academic study ?
R : Yes, it is an excellent resource for advanced students in actuarial science, statistics, and finance. It bridges the gap between theory and practice, making it a valuable supplement to academic textbooks.
Q : What is the ISBN of this volume ?
R : The ISBN for the hardcover edition is 9781107133796.
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