Chapter Preview. 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 them to predict future outcomes. The goal of this two-volume set is to build on the training of actuaries by developing the fundamentals of predictive modeling and providing corresponding applications in actuarial science, risk management, and insurance. This introduction sets the stage for these volumes by describing the conditions that led to the need for predictive modeling in the insurance industry. It then traces the evolution of predictive modeling that led to the current statistical methodologies that prevail in actuarial science today.

Introduction

A classic definition of an actuary is “one who determines the current financial impact of future contingent events.” Actuaries are typically employed by insurance companies who job is to spread the cost of risk of these future contingent events.

The day-to-day work of an actuary has evolved over time. Initially, the work involved tabulating outcomes for “like” events and calculating the average outcome. For example, an actuary might be called on to estimate the cost of providing a death benefit to each member of a group of 45-year-old men. As a second example, an actuary might be called on to estimate the cost of damages that arise from an automobile accident for a 45-year-old driver living in Chicago.