Predictive Modeling Applications in Actuarial Science
- Volume 1
- Introduction
- Predictive Modeling Foundations
- Predictive Modeling Methods
- Bayesian and Mixed Modeling
- Longitudinal Modeling
- Volume 2
- Generalized Linear Model
- Extensions of the Generalized Linear Model
- Unsupervised Predictive Modeling Methods
-
Applications on Current Problems in Actuarial Science
- Chapter 8 - The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
- Chapter 9 - Finite Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
- Chapter 10 - A Framework for Managing Claim Escalation Using Predictive Modeling
- Chapter 11 - Predictive Modeling for Usage-Based Auto Insurance
Chapter 1 - Predictive Modeling in Actuarial Science
Authors
Edward W. Frees | University of Wisconsin - Madison
jfrees@bus.wisc.edu
Richard A. Derrig | Opal Consulting LLC
richard@derrig.com
Glenn Meyers
ggmeyers@metrocast.net
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-set volume 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.