Description: Discussion of machine learning applications currently being used to augment traditional actuarial modeling in the life insurance industry. This includes data science techniques for: Quantifying and explaining differences between model versions or reporting periods Building proxy models for actuarial model output using machine learning One-class classification for data integrity checks Applications of unsupervised learning (e.g., clustering and principal components analysis) to reduce model run time

Description: Incorporating Pended Claims in Medical IBNR Estimates. Issue No. 45.

Description: Article What Actuaries Need To Know About Constructing Mortality Tables by Jones in The Actuary, February 1979, Volume 13, Number 2

Description: We provide a possible solution to deal with the parameter estimation of mortality models when the population size is small, based on graduation methods.

Description: This paper discusses the actuarial risk measures and uses a variety of estimation techniques such as nonparametric approach, parametric methods and robust procedures. From 39th Actuarial Research Conference, Iowa City, Iowa, August 5-7, 2004.

Description: The purpose of this paper is to introduce a practical method for interpolation and smoothing and give a number of numerical examples using the method. The method is a variation of the Whittaker-Henderson method. Discussions of this paper are included.

Description: This paper will investigate the problem in estimating mortality rates using data obtained from insurance company records.

Description: Exam study notes on credibility theory.

Description: In this paper, we study a survival regression model known as Cox's proportional Hazards model. We assume that the data available are already grouped and develop a minimum distance estimation procedure that is also applicable when the data are also left or right censored. Asymptotic properties of the estimators are established and the numerical implementation of the method based on an iterated reweighted least squares algorithm is discussed.

Description: We apply decision theory to develop a credibility formula that minimizes a loss function that is a linear combination of a squared-error term and a second-derivative term. The squared-error term measures the accuracy of the estimator, while the second-derivative term constrains the estimator to be close to linear.