  Undoubtedly, several countries worldwide endure to experience a continuous
increase in life expectancy, extending the challenges of life actuaries and
demographers in forecasting mortality. Although several stochastic mortality
models have been proposed in past literature, the mortality forecasting
research remains a crucial task. Recently, various research works encourage the
adequacy of deep learning models to extrapolate suitable pattern within
mortality data. Such a learning models allow to achieve accurate point
predictions, albeit also uncertainty measures are necessary to support both
model estimates reliability and risk evaluations. To the best of our knowledge,
machine and deep learning literature in mortality forecasting lack for studies
about uncertainty estimation. As new advance in mortality forecasting, we
formalizes the deep Neural Networks integration within the Lee-Carter
framework, posing a first bridge between the deep learning and the mortality
density forecasts. We test our model proposal in a numerical application
considering three representative countries worldwide and both genders,
scrutinizing two different fitting periods. Exploiting the meaning of both
biological reasonableness and plausibility of forecasts, as well as performance
metrics, our findings confirm the suitability of deep learning models to
improve the predictive capacity of the Lee-Carter model, providing more
reliable mortality boundaries also on the long-run.
