  The last two centuries have seen a significant increase in life expectancy.
Although past trends suggest that mortality will continue to decline in the
future, uncertainty and instability about the development is greatly increased
due to the ongoing COVID-19 pandemic. It is therefore of essential interest,
particularly to annuity and life insurers, to predict the mortality of their
members or policyholders with reliable accuracy. The goal of this study is to
improve the state-of-the-art stochastic mortality models using machine learning
techniques and generalize them to a multi-population model. Detailed
cross-country results conducted for Finland, Germany, Italy, the Netherlands,
and the United States show that the best forecasting performance is achieved by
a generalized additive model that uses the framework of APC analysis. Based on
this finding, trend forecasts of mortality rates as a measure of longevity are
fulfilled for the future, given a range of COVID-19 scenarios, from mild to
severe. Discussing and evaluating the plausibility of these scenarios, this
study is useful for preparation, planning, and informed decision-making.
