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Bayesian Neural Networks for Actuarial Mortality Modelling
Thesis (MCom)--Stellenbosch University, 2026.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2026
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| _version_ | 1867614037870641152 |
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| access_status_str | Open Access |
| author | Moehrke, Patrick Gary |
| author2 | Bierman, S. |
| author_browse | Bierman, S. Moehrke, Patrick Gary |
| author_facet | Bierman, S. Moehrke, Patrick Gary |
| author_sort | Moehrke, Patrick Gary |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MCom)--Stellenbosch University, 2026. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/136197 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:45:40.774Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/136197 Bayesian Neural Networks for Actuarial Mortality Modelling Moehrke, Patrick Gary Bierman, S. Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistical and Actuarial Science. Thesis (MCom)--Stellenbosch University, 2026. Moehrke, P. G. 2026. Bayesian Neural Networks for Actuarial Mortality Modelling. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/c1816a5a-366c-487b-a335-c67576128890 The use of Bayesian neural networks (BNNs) for mortality modelling is an understudied, yet potentially promising area of research. They inherently offer robust uncertainty quantification, and are known for their application to sparse or small datasets. This research investigates the efficacy of BNN architectures in the presence of extreme data sparsity and explores methodologies for incorporating domain-specific prior information through hybrid structures. By embedding classical mortality laws directly into the neural network framework, we develop a suite of hybrid models capable of leveraging both the flexibility of deep learning, and the interpretability of parametric actuarial laws. We demonstrate that these Bayesian hybrid models provide promising forecasting capabilities and produce reliable uncertainty intervals, even under conditions of severe data sparsity. More specifically, our results indicate that these hybrid models can accurately capture underlying mortality patterns and interpolate missing values, even when 95% of the training data is absent. The performance of this mortality prediction approach is evaluated using female population data from a diverse set of countries, namely Iceland, Italy, Japan, Russia, Sweden, and the United States. Furthermore, we extend the above framework also to multi-population modelling. This is done by proposing an architecture that utilises embedding layers on population indices. These embeddings function as population-specific loading factors for the global terms within the hybrid models, allowing joint modelling across heterogeneous cohorts. While these models perform well on sparse multi-population data, we discuss the computational and runtime constraints encountered when scaling BNNs to comprehensive multi-population datasets. Masters 2026-04-24T12:59:17Z 2026-04-24T12:59:17Z 2026-03 Thesis https://scholar.sun.ac.za/handle/10019.1/136197 en Stellenbosch University 125 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Moehrke, Patrick Gary Bayesian Neural Networks for Actuarial Mortality Modelling |
| title | Bayesian Neural Networks for Actuarial Mortality Modelling |
| title_full | Bayesian Neural Networks for Actuarial Mortality Modelling |
| title_fullStr | Bayesian Neural Networks for Actuarial Mortality Modelling |
| title_full_unstemmed | Bayesian Neural Networks for Actuarial Mortality Modelling |
| title_short | Bayesian Neural Networks for Actuarial Mortality Modelling |
| title_sort | bayesian neural networks for actuarial mortality modelling |
| url | https://scholar.sun.ac.za/handle/10019.1/136197 |
| work_keys_str_mv | AT moehrkepatrickgary bayesianneuralnetworksforactuarialmortalitymodelling |