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Predictive Modeling Applications in Actuarial Science
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  • Cited by 6
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    This (lowercase (translateProductType product.productType)) has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Henckaerts, Roel Antonio, Katrien Clijsters, Maxime and Verbelen, Roel 2018. A data driven binning strategy for the construction of insurance tariff classes. Scandinavian Actuarial Journal, p. 1.

    Bazán, J.L. Torres-Avilés, F. Suzuki, A.K. and Louzada, F. 2017. Power and reversal power links for binary regressions: An application for motor insurance policyholders. Applied Stochastic Models in Business and Industry, Vol. 33, Issue. 1, p. 22.


    van Berkum, Frank Antonio, Katrien and Vellekoop, Michel 2017. A BAYESIAN JOINT MODEL FOR POPULATION AND PORTFOLIO-SPECIFIC MORTALITY. ASTIN Bulletin, Vol. 47, Issue. 03, p. 681.

    Duncan, I. Loginov, M. and Ludkovski, M. 2016. Testing Alternative Regression Frameworks for Predictive Modeling of Health Care Costs. North American Actuarial Journal, Vol. 20, Issue. 1, p. 65.

    Christiansen, Marcus C. and Schinzinger, Edo 2016. A Credibility Approach for Combining Likelihoods of Generalized Linear Models. ASTIN Bulletin, Vol. 46, Issue. 03, p. 531.

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    Predictive Modeling Applications in Actuarial Science
    • Online ISBN: 9781139342674
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Book description

Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.


'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 P and C actuary whose main concern is using predictive modeling in his or her own work.'

David Zornek Source: Actuarial Review

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