Hierarchical forecasting of causes of death with trend breaks in mortality modeling: Kenyan case
Trends offer direction and momentum. However, trends in mortality are affected by trend breaks, which are a consequence of mortality shocks. Additionally, insufficient historical data challenge the credibility of the forecasted trends, which are useful for actuaries in pricing, reserving, and valuin...
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LLC "CPC "Business Perspectives"
2025-01-01
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| Series: | Insurance Markets and Companies |
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| Online Access: | https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/21457/IMС_2025_01_Bett.pdf |
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| author | Nicholas Bett Juma Kasozi Daniel Ruturwa |
| author_facet | Nicholas Bett Juma Kasozi Daniel Ruturwa |
| author_sort | Nicholas Bett |
| collection | DOAJ |
| description | Trends offer direction and momentum. However, trends in mortality are affected by trend breaks, which are a consequence of mortality shocks. Additionally, insufficient historical data challenge the credibility of the forecasted trends, which are useful for actuaries in pricing, reserving, and valuing life insurance products. To address these challenges, the study aims to determine and incorporate trend breaks among individual causes of death and coherently forecast them by applying the bottom-up hierarchical forecasting approach for life insurance models. The models used are categorized as base (linear model), auto-statistical (Arima, Exponential-Smoothing, and Prophet), and auto-machine learning. The data from the World Health Organization consisted of annualized mortality quantities by cause, gender, age, and period for Kenya. Results based on the mean absolute percentage error criteria across the causes of death showed that all the models apart from the base model showed significant improvement after accounting for the trend breaks with the best being the auto machine learning approach leading with seven causes of death. Updating forecasts based on the computed trend breakpoints that varied between 2007 to 2011 generally improved forecast accuracy. These results suggest that forecasting errors may be reduced after accounting for trend breaks and model specifications. Furthermore, this implies that insufficient data do not necessarily produce deficient forecasts. The study’s contribution involved applying approaches that enhance the accuracy of forecasting models to prevent adverse effects of mortality shocks in actuarial modeling. |
| format | Article |
| id | doaj-art-e255bfbaa33a46d28c39c33f31a863bd |
| institution | Kabale University |
| issn | 2616-3551 2522-9591 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | LLC "CPC "Business Perspectives" |
| record_format | Article |
| series | Insurance Markets and Companies |
| spelling | doaj-art-e255bfbaa33a46d28c39c33f31a863bd2025-01-21T14:48:46ZengLLC "CPC "Business Perspectives"Insurance Markets and Companies2616-35512522-95912025-01-01161153210.21511/ins.16(1).2025.0221457Hierarchical forecasting of causes of death with trend breaks in mortality modeling: Kenyan caseNicholas Bett0https://orcid.org/0000-0001-9376-7398Juma Kasozi1https://orcid.org/0000-0002-0941-9604Daniel Ruturwa2https://orcid.org/0000-0001-7780-3993MSc., Junior Research Fellow, African Centre of Excellence in Data Science (ACEDS), College of Business and Economics, University of Rwanda, RwandaAfrican Centre of Excellence in Data Science, University of Rwanda, Rwanda; Ph.D., Professor, Department of Mathematics, Makerere University, UgandaSenior Lecturer, Department of Applied Statistics, School of Economics, University of Rwanda, RwandaTrends offer direction and momentum. However, trends in mortality are affected by trend breaks, which are a consequence of mortality shocks. Additionally, insufficient historical data challenge the credibility of the forecasted trends, which are useful for actuaries in pricing, reserving, and valuing life insurance products. To address these challenges, the study aims to determine and incorporate trend breaks among individual causes of death and coherently forecast them by applying the bottom-up hierarchical forecasting approach for life insurance models. The models used are categorized as base (linear model), auto-statistical (Arima, Exponential-Smoothing, and Prophet), and auto-machine learning. The data from the World Health Organization consisted of annualized mortality quantities by cause, gender, age, and period for Kenya. Results based on the mean absolute percentage error criteria across the causes of death showed that all the models apart from the base model showed significant improvement after accounting for the trend breaks with the best being the auto machine learning approach leading with seven causes of death. Updating forecasts based on the computed trend breakpoints that varied between 2007 to 2011 generally improved forecast accuracy. These results suggest that forecasting errors may be reduced after accounting for trend breaks and model specifications. Furthermore, this implies that insufficient data do not necessarily produce deficient forecasts. The study’s contribution involved applying approaches that enhance the accuracy of forecasting models to prevent adverse effects of mortality shocks in actuarial modeling.https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/21457/IMС_2025_01_Bett.pdfactuarialbottom-uplongevitymachine learningmortality shockrisk management |
| spellingShingle | Nicholas Bett Juma Kasozi Daniel Ruturwa Hierarchical forecasting of causes of death with trend breaks in mortality modeling: Kenyan case Insurance Markets and Companies actuarial bottom-up longevity machine learning mortality shock risk management |
| title | Hierarchical forecasting of causes of death with trend breaks in mortality modeling: Kenyan case |
| title_full | Hierarchical forecasting of causes of death with trend breaks in mortality modeling: Kenyan case |
| title_fullStr | Hierarchical forecasting of causes of death with trend breaks in mortality modeling: Kenyan case |
| title_full_unstemmed | Hierarchical forecasting of causes of death with trend breaks in mortality modeling: Kenyan case |
| title_short | Hierarchical forecasting of causes of death with trend breaks in mortality modeling: Kenyan case |
| title_sort | hierarchical forecasting of causes of death with trend breaks in mortality modeling kenyan case |
| topic | actuarial bottom-up longevity machine learning mortality shock risk management |
| url | https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/21457/IMС_2025_01_Bett.pdf |
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