Machine learning in long-term mortality forecasting

Yang Qiao, Chou-Wen Wang and Wenjun Zhu ()
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Yang Qiao: National Sun Yat-Sen University
Chou-Wen Wang: National Sun Yat-Sen University
Wenjun Zhu: Nanyang Technological University

The Geneva Papers on Risk and Insurance - Issues and Practice, 2024, vol. 49, issue 2, No 7, 340-362

Abstract: Abstract We propose a new machine learning-based framework for long-term mortality forecasting. Based on ideas of neighboring prediction, model ensembling, and tree boosting, this framework can significantly improve the prediction accuracy of long-term mortality. In addition, the proposed framework addresses the challenge of a shrinking pattern in long-term forecasting with information from neighboring ages and cohorts. An extensive empirical analysis is conducted using various countries and regions in the Human Mortality Database. Results show that this framework reduces the mean absolute percentage error (MAPE) of the 20-year forecasting by almost 50% compared to classic stochastic mortality models, and it also outperforms deep learning-based benchmarks. Moreover, including mortality data from multiple populations can further enhance the long-term prediction performance of this framework.

Keywords: Long-term mortality forecasting; Neighborhood effect; Machine learning; Ensemble models; Tree boosting; Longevity risk (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1057/s41288-024-00320-5

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