Machine learning techniques for mortality modeling

Various stochastic models have been proposed to estimate mortality rates. In this paper we illustrate how machine learning techniques allow us to analyze the quality of such mortality models. In addition, we present how these techniques can be used for differentiating the different causes of death in mortality modeling.

[1]  Pavel V. Shevchenko,et al.  Actuarial Applications and Estimation of Extended~CreditRisk$^+$ , 2015, 1505.04757.

[2]  T. Therneau,et al.  An Introduction to Recursive Partitioning Using the RPART Routines , 2015 .

[3]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[4]  David Blake,et al.  A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States , 2009 .

[5]  M. Wüthrich.  Non-Life Insurance: Mathematics & Statistics , 2017 .

[6]  Steven Haberman,et al.  A cohort-based extension to the Lee-Carter model for mortality reduction factors , 2006 .

[7]  Pavel V. Shevchenko,et al.  Crunching Mortality and Life Insurance Portfolios with Extended CreditRisk , 2016 .

[8]  Andrés M. Villegas,et al.  StMoMo: An R Package for Stochastic Mortality Modelling , 2018 .

[9]  Steven Haberman,et al.  Lee-Carter mortality forecasting: a parallel generalized linear modelling approach for England and Wales mortality projections , 2003 .

[10]  H. Stommel,et al.  Volcano weather: the story of 1816, the year without a summer [Indonesia] , 1983 .

[11]  Ronald Lee,et al.  Modeling and forecasting U. S. mortality , 1992 .

[12]  S. Richards.  Selected Issues in Modelling Mortality by Cause and in Small Populations , 2009, British Actuarial Journal.

[13]  Mario V. Wuthrich,et al.  Data Analytics for Non-Life Insurance Pricing , 2019 .

[14]  Daniel H. Alai,et al.  Modelling cause-of-death mortality and the impact of cause-elimination , 2013, Annals of Actuarial Science.