AbstractIn recent decades, analysing the progression of mortality rates has become very important for both public and private pension schemes, as well as for the life insurance branch of insurance companies. Traditionally, the tools used in this field were based on stochastic and deterministic approaches that allow extrapolating mortality rates beyond the last year of observation. More recently, new techniques based on machine learning have been introduced as alternatives to traditional models, giving practitioners new opportunities. Among these, neural networks (NNs) play an important role due to their computation power and flexibility to treat the data without any probabilistic assumption. In this paper, we apply multi-task NNs, whose approach is based on leveraging useful information contained in multiple related tasks to help improve the generalized performance of all the tasks, to forecast mortality rates. Finally, we compare the performance of multi-task NNs to that of existing single-task NNs and traditional stochastic models on mortality data from 17 different countries.