Seminar Overview
Predictive modeling is an idea whose time has come. Simply put, predictive modeling is a process by which one uses statistical analysis of data to make predictions about future events or otherwise gain actionable insights about multidimensional problems. Predictive modeling can therefore serve as an aid to human reasoning, and, as such, has nearly unlimited applications.

Chain stores use it to select their next store's location. Baseball teams use it to recruit "undervalued" players. Predictive modeling helps dot-com retailers predict their customers' next likely purchases and governments detect instances of corporate corruption. Colleges and universities use it to predict which potential students will remain enrolled until graduation. Insurers can also use predictive modeling to select and retain optimal portfolios of policyholders.

While predictive modeling has been around for a long time, notably in the form of regression analysis, recent advances in statistics and the proliferation of cheap computing power have dramatically increased the practicality and applicability of predictive modeling. As a result, the application of predictive modeling to the actuarial profession is now limited only by its members' imaginations.

The purpose of this seminar is twofold. The first goal is to promote a better understanding of predictive modeling techniques in the actuarial community. The second is to further discussion of current and future insurance applications of predictive models.

Basic- and intermediate-level sessions will be offered covering such predictive modeling and analytic techniques as:

• Generalized Linear Models (GLM)
• Classification And Regression Trees (CART)
• Multivariate Adaptive Regression Splines (MARS)
• Neural Networks
• Generalized Additive Models (GAM)
• Clustering
• Principal Components Analysis
• Bootstrapping
• Model Validation

Credit scoring is the best-known application of predictive modeling in insurance. This innovation has served as an early indication of the power of data mining and predictive modeling in insurance. Personal and commercial lines insurers are increasingly applying predictive modeling techniques in areas as diverse as marketing, underwriting, rating, fraud detection, retention and cross-sell analyses, and identifying appropriate case reserves for claims. Key topics of discussion will be data sources, business strategies behind predictive modeling projects, and model implementation.