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Predictive Modeling Applications in Actuarial Science: Volume 2, Case Studies in Insurance (International Series on Actuarial Science)

By: Edward W. Frees (Edited by) , Glenn Meyers (Edited by) , Richard A. Derrig (Edited by)

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Ksh 22,550.00

Format: Hardback or Cased Book

ISBN-10: 1107029880

ISBN-13: 9781107029880

Collection / Series: International Series on Actuarial Science

Collection Type: Publisher collection

Publisher: Cambridge University Press

Imprint: Cambridge University Press

Country of Manufacture: US

Country of Publication: GB

Publication Date: Jul 27th, 2016

Publication Status: Active

Product extent: 330 Pages

Weight: 748.00 grams

Dimensions (height x width x thickness): 25.70 x 24.70 x 2.30 cms

Product Classification / Subject(s): Insurance & actuarial studies

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Predictive modeling involves the use of data to forecast future events. Building on the foundations developed in the first volume, Volume 2 examines applications of predictive modeling, focusing on property and casualty insurance, exposing readers to a variety of techniques in real-life contexts that demonstrate the value of predictive modeling.
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.

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