Description: The basic principle introduced in this paper is that the expected value of receipts on any insurance policy is equal to the expected value of payouts. Using an expected value approach, rather than present values, offers an interesting conceptual framework which might provide a possible starting point in risk theory to consider statistical variation about these expected values.

Description: This article explores the possibility of using machine learning to improve, or potentially rewrite, actuarial code within actuarial software.

Description: Description of the vast variety of predictive analytics topics covered by the Forecasting & Futurism section and introduction to the July 2015 issue of the newsletter.

Description: Continuation of article from previous issue. This article illustrates the practical and financial implications of applying nested stochastic techniques to a sample product.

Description: It is worth the effort to obtain a pricing surface. It helps make an informed decision and facilitate the pricing conversation within and outside a company.

Description: Presentation discussing modeling of daily and hourly rainfall, both occurrence and amount. These distributions would be needed to price derivative or insurance contracts involving rainfall amounts.

Description: This 1979 session is about recent advances in Prediction Theory. Topics included: 1. Statistical methods to predict path properties of a Time Series. 2. How and to what extent Box-Jenkins Time Series methods can be modified to predict two or more time series simultaneously. 3. Workable techniques that are already being successfully used in practice.

Description: This teaching session is about conveying to users of models and forecasts how uncertain the outcome of stochastic projections really is.

Description: This article discusses the need for a sophisticated modeling system capable of analyzing the impact of the variables present in pension plan projections and providing management with information which it may act upon.

Description: Provides details of the 2nd forecasting competition for the best model that predicts the Civilian Unemployment Rate 'UNRATE', measured on a monthly basis, from March-end 2012 through September-end 2012.