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 Workersâ€™ Compensation LargeLoss Regression Analysis
 Chapter 10  A Framework for Managing Claim Escalation Using Predictive Modeling
 Chapter 11  Predictive Modeling for UsageBased Auto Insurance
Chapter 18  Claims Triangles/Loss Reserves
Authors
Greg Taylor  University of New South Wales
greg_taylor60@hotmail.com
Chapter Preview
This chapter considers the application of predictive models to insurance claims triangles and the associated prediction problem of loss reserving (Section 18.1).
This is approached initially by reference to the chain ladder, a widely used heuristic reserving algorithm. Rigorous predictive models, in the form of Generalized Linear Models, that reproduce this algorithm are explored (Section 18.2).
The chain ladder has a restricted model structure and a number of embellishments are considered (Section 18.3). These include the incorporation in the model of accident year effects through the use of exposure data (e.g. accident year claim counts) (Section 18.3.2), and also the incorporation of claim closure data (Section 18.3.3).
A subsequent section considers models that incorporate claim closure data on an operational time basis (Section 18.3.4). In each of these cases emphasis is placed on the ease of inclusion of these model features in the GLM structure.
Hitherto, all models in this chapter have related to conventional claims triangles. These data sets are aggregate, as opposed to unit record claim data sets that record detail of individual claims. The chapter closes with a brief introduction to individual claim models (18.4). On occasion these use survival analysis as well as GLMs.