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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.
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
practicing analysts who would like an overview of advanced
statistical topics that are particularly relevant in actuarial
practice. 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 by
analysts to gain a competitive advantage in situations with complex
data."
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