Description: As a life reinsurer, understanding mortality risk is mission critical. Superior models of mortality risk lead to more competitive pricing in profitable market segments and more efficient use of capital. Surprisingly, modern statistical tools such as predictive modeling have not previously been used to model mortality data to gain this edge. In this paper, we present the use of a predictive modeling technique called projection pursuit regression PPR to model mortality data. We first describe the process of creating a PPR model using inter-company mortality data collected by the Individual Life Experience Committee of the Society of Actuaries, and present the resulting model. We then discuss the process of using this model as a starting point for creating a more detailed PPR model of internal company mortality data.

Description: These are the slides that go with the paper 'Mortality Modeling using Projection Pursuit Regression.' In that paper, the authors develop sixteen separate mortality models based on the current SOA mortality data being used for the preferred life study. The models are constructed using projection pursuit regression. The sixteen models arise from all combinations of sex, smoker status, select and ultimate status, and per life vs. per dollar mortality rates. In general the models confirm the following four facts: 1 Mortality rates increase as attained age increases. 2 Mortality rates increase as duration increases. 3 Mortality rates decrease as Face Amount Bands increase. 4. Mortality rates decrease if preferred underwriting is present.