Application of Machine Learning to Mortality Modeling and Forecasting
Estimation of future mortality rates still plays a central role among life insurers in
pricing their products and managing longevity risk. In the literature on mortality modeling, a wide
number of stochastic models have been proposed, most of them forecasting future mortality
rates by extrapolating one or more latent factors. The abundance of proposed models shows that
forecasting future mortality from historical trends is non-trivial. Following the idea proposed in
Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly
identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit
of standard stochastic mortality models. The machine learning estimator is then forecasted according
to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard
stochastic models. Out-of sample forecasts are provided to verify the model accuracy.