City Research Online

Abstract

In 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 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 neural networks, 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 neural networks to that of existing single-task neural networks and traditional stochastic models on mortality data from seventeen different countries.

Publication Type:	Article
Additional Information:	This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. © The Author(s), 2025. Published by Cambridge University Press on behalf of The International Actuarial Association
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HG Finance Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Bayes Business School Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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