Mortality Improvements in South Africa: Insights from Pensioner Mortality

Abstract

The study of mortality improvements in South Africa has been complicated by data limitations: from a population perspective, the data are incomplete and misreported whereas pooled data collected from insurance companies for industry studies often span short time periods and exhibit significant heterogeneity. Notwithstanding these issues, accurate quantification of mortality improvement is critical for actuarial valuations of life insurers and pension funds. In this study, we analyse a unique pensioner dataset, covering the years 2000-2019, through the dual lenses of traditional actuarial analysis and deep learning techniques. We report on aggregate mortality improvements, as well as how mortality improvements differ depending on gender and pension amount. We investigate whether the observed mortality improvements might be attributed to changes in the composition of the pensioner dataset by contrasting these results with those derived from a more complex model that allows for these changes over time. Finally, we show the estimated impact of the observed improvements on pension liabilities and calibrate models to provide uncertainty around the estimated mortality improvement rates, to benchmark the SAM longevity stress and provide a basis for IFRS 17 risk adjustment calculations.

Keywords: Mortality, Lee Carter model, Neural Networks, Deep Learning, IFRS 17, Uncertainty quantification

JEL Classification: G22