Colloque / Séminaire

SEMINAIRE LABO, José GARRIDO, Professor of Mathematics and Statistics, Concordia University, Montréal

Deep neural networks with long short–term memory for human mortality modeling

Accurate modeling and forecasting of human mortality rates is important in actuarial science, to price life insurance products, pension plan evaluations, and in finance, to price derivative products used to hedge longevity risk.

Data shows that mortality rates have been decreasing at all ages over time, especially in the last century. Predicting the extent of future longevity improvement represents a difficult and important problem for the life insurance industry and for sponsors of pension plans and social security programs.

The most popular methodology to forecast future mortality improvement was proposed by Lee and Carter (1992, JASA). It consists of a two-steps process, shown to suffer identifiability issues, both in the Lee-Carter Model and its subsequent extensions, mostly due to the inherent two-steps model setup.
We propose a very distinct, data-driven approach using a class of Deep Neural Networks to model and forecast human mortality.
The main component in the neural networks is a long short–term memory (NNLS-TM) layer, which was introduced by Hochreiter and Schmidhuber (1997, NC), to fix vanishing gradients in simple recurrent neural networks. The model can be constructed for short–term as well as for long-term forecasting, respectively.

We model the dependence mortality improvement observed simultaneously in different countries. Current mortality improvement models are fitted to single country sub-populations separately, even if improvement trends are similar in different countries.
The multi–population problem presents serious computational challenges that we tackle with NNLS-TMs, fitted to learn from single country populations included in the Human Mortality Database (

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