Mortality forecasting via multi-task neural networks

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      <subfield code="a">Understanding and projecting mortality dynamics has become increasingly critical for pension systems and life insurers. While traditional stochastic and deterministic models have long been used to extrapolate mortality patterns beyond observed data, recent advances in machine learning offer new modelling opportunities. In particular, neural networks (NNs) provide strong computational capabilities and the flexibility to capture complex relationships without relying on explicit probabilistic assumptions. In this study, they develop a multi-task neural network framework that jointly learns from multiple related populations, enabling shared information to enhance forecasting accuracy across countries. Using mortality data from 17 nations, they evaluate the performance of these multi-task NNs against both single-task NN architectures and established stochastic mortality models. Their findings highlight the potential of multi-task learning to improve long-term mortality projections and support actuarial applications.</subfield>
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