Skip to content
Register Sign in Wishlist

Predictive Modeling Applications in Actuarial Science

Volume 2. Case Studies in Insurance

$99.99 (C)

Award Winner

Part of International Series on Actuarial Science

Ernesto Schirmacher, Dan Tevet, Greg Taylor, James Sullivan, Peng Shi, James Guszczaz, Mona S. A. Hammad, Galal A. H. Harby, Ji Yao, Louise A. Francis, Glenn Meyers, Luyang Fu, Xianfang Liu, Mohamad A. Hindawi, Claudine H. Modlin, Udi Makov, Jim Weiss
View all contributors
  • Date Published: July 2016
  • availability: Available
  • format: Hardback
  • isbn: 9781107029880

$ 99.99 (C)

Add to cart Add to wishlist

Other available formats:

Looking for an examination copy?

This title is not currently available for examination. However, if you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact providing details of the course you are teaching.

Product filter button
About the Authors
  • 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.

    • Supports active and life-long learning through the use of real world data sets
    • Introduces advanced techniques that can be used to gain a competitive advantage in situations with complex data
    • Provides a link between data analysis and data modeling, explaining the role of a model
    Read more


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

    Reviews & endorsements

    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, Actuarial Review

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity


    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?


    Product details

    • Date Published: July 2016
    • format: Hardback
    • isbn: 9781107029880
    • length: 330 pages
    • dimensions: 254 x 177 x 21 mm
    • weight: 0.73kg
    • availability: Available
  • Table of Contents

    1. Pure premium modeling using generalized linear models Ernesto Schirmacher
    2. Applying generalized linear models to insurance data - frequency-severity vs pure premium modeling Dan Tevet
    3. GLMs as predictive claim models Greg Taylor and James Sullivan
    4. Frameworks for general insurance ratemaking - beyond the generalized linear model Peng Shi and James Guszczaz
    5. Using multilevel modeling for group health insurance ratemaking - a case study from the Egyptian market Mona S. A. Hammad and Galal A. H. Harby
    6. Clustering in general insurance pricing Ji Yao
    7. Advanced unsupervised learning methods applied to insurance claims data Louise A. Francis
    8. The predictive distribution of loss reserve estimates over a finite time horizon Glenn Meyers
    9. Finite mixture model and workers compensation large loss regression analysis Luyang Fu and Xianfang Liu
    10. A framework for managing claim escalation using predictive modeling Mohamad A. Hindawi and Claudine H. Modlin
    11. Predictive modeling for usage-based auto insurance Udi Makov and Jim Weiss.

  • Resources for

    Predictive Modeling Applications in Actuarial Science

    Find resources associated with this title

    Type Name Unlocked * Format Size

    Showing of

    Back to top

    This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to instructors whose faculty status has been verified. To gain access to locked resources, instructors should sign in to or register for a Cambridge user account.

    Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other instructors may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.

    Supplementary resources are subject to copyright. Instructors are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.

    If you are having problems accessing these resources please contact

  • Editors

    Edward W. Frees, University of Wisconsin, Madison
    Edward W. (Jed) Frees is the Hickman-Larson Chair of Actuarial Science at the University of Wisconsin, Madison. He received his PhD in Mathematical Statistics in 1983 from the University of North Carolina, Chapel Hill and is a Fellow of both the Society of Actuaries (SoA) and the American Statistical Association (the only Fellow of both organizations). Regarding his research, Professor Frees has won several awards for the quality of his work, including the Halmstad Prize for best paper published in the actuarial literature (four times).

    Glenn Meyers, ISO Innovative Analytics, New Jersey
    Glenn Meyers, FCAS, MAAA, CERA, and PhD, retired from ISO at the end of 2011 after a 37 year career as an actuary. He holds a BS in Mathematics and Physics from Alma College, Michigan, an MA in Mathematics from Oakland University, Michigan, and a PhD in Mathematics from the State University of New York, Albany. A frequent speaker at Casualty Actuarial Society (CAS) meetings, he has served and continues to serve the CAS and the International Actuarial Association on various research and education committees. He has also served on the CAS Board of Directors. He has several published articles in the Proceedings of the Casualty Actuarial Society, Variance and the Actuarial Review. His research contributions have been recognized by the CAS through his being a three-time winner of the Woodward–Fondiller Prize, a two-time winner of the Dorweiler Prize, the DFA Prize, the Reserves Prize, the Matthew Rodermund Service Award and the Michelbacher Significant Achievement Award. In retirement he still spends some of his time on his continuing passion for actuarial research.

    Richard A. Derrig, Temple University, Philadelphia
    Richard A. Derrig is founder and principal of OPAL Consulting LLC., a firm that provides research and regulatory support to property casualty insurance clients. Primary areas of expertise include financial pricing models, database and data mining design, fraud detection planning and implementation, and expert testimony for regulation and litigation purposes.


    Ernesto Schirmacher, Dan Tevet, Greg Taylor, James Sullivan, Peng Shi, James Guszczaz, Mona S. A. Hammad, Galal A. H. Harby, Ji Yao, Louise A. Francis, Glenn Meyers, Luyang Fu, Xianfang Liu, Mohamad A. Hindawi, Claudine H. Modlin, Udi Makov, Jim Weiss


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

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner Please see the permission section of the catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×
warning icon

Turn stock notifications on?

You must be signed in to your Cambridge account to turn product stock notifications on or off.

Sign in Create a Cambridge account arrow icon

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close

Are you sure you want to delete your account?

This cannot be undone.


Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

Please fill in the required fields in your feedback submission.