Predictive modeling is increasingly being used by actuaries to solve a wide range of problems, such as designing plans, predicting loss development, and analyzing customer retention. This course will introduce the audience to the basic concepts of actuarial predictive modeling and provide several hands-on examples of how to construct a model and interpret model output.

Kevin Strous, Nationwide "This is a very good intro to predictive modeling."

LEARNING OBJECTIVES

• How predictive modeling is commonly used in the insurance industry and other industries
• How Generalized Linear Models (GLM) are used to build insurance rating plans
• How to set up a GLM and interpret model output
• How to assess the performance of a predictive model and refine it accordingly

Becky Roever, Ameriprise Auto & Home Insurance "I thought this was a great course on the basics of predictive modeling for insurance. It was easy to understand."

INTENDED AUDIENCE
This course is targeted toward an actuary with 4+ years of experience in the insurance industry and at least an ACAS designation (or equivalent knowledge). The content is ratemaking-focused, but an experienced actuary in any practice area should benefit from this knowledge.
In particular, a course attendee should have the following level of knowledge:

• How insurance rating plans typically work (base rates, exposure units, and rating factors)
• How actuaries have traditionally calculated classification relativities via minimum bias procedures
• The characteristics of a typical insurance dataset, including how claim frequency, claim severity, and pure premium are calculated
• How simple linear (least squares) regression works

Dave Brueckman, Frankenmuth Insurance "Highly recommend this course for those seeking a basic understanding of GLM modeling. I found this particularly good in recapping the key points in developing a multivariate class plan model."