Application of Machine learning Techniques to the Calculation of Solvency Capital Requirements

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dc.contributor.advisorBotha, Pieter
dc.contributor.authorBlake, Gareth
dc.date.accessioned2026-06-25T09:53:19Z
dc.date.available2026-06-25T09:53:19Z
dc.date.issued2026
dc.date.updated2026-06-25T09:52:20Z
dc.description.abstractThe application of machine learning in actuarial science is a rapidly expanding field that bridges traditional actuarial methods with emerging data-driven techniques. This paper examines how machine learning can be used to calculate an insurance company's Solvency Capital Requirement (SCR). Various machine learning models were trained and tested to assess their predictive accuracy for the SCR across different risk scenarios. The findings indicate that machine learning approaches can reliably forecast the SCR, although interpretability challenges must be addressed due to the complex nature of these models. This work contributes to the existing literature on the intersection of traditional actuarial practices and modern machine learning methodologies.
dc.identifier.apacitationBlake, G. (2026). <i>Application of Machine learning Techniques to the Calculation of Solvency Capital Requirements</i>. (). University of Cape Town ,Faculty of Commerce ,School of Management Studies. Retrieved from http://hdl.handle.net/11427/43384en_ZA
dc.identifier.chicagocitationBlake, Gareth. <i>"Application of Machine learning Techniques to the Calculation of Solvency Capital Requirements."</i> ., University of Cape Town ,Faculty of Commerce ,School of Management Studies, 2026. http://hdl.handle.net/11427/43384en_ZA
dc.identifier.citationBlake, G. 2026. Application of Machine learning Techniques to the Calculation of Solvency Capital Requirements. . University of Cape Town ,Faculty of Commerce ,School of Management Studies. http://hdl.handle.net/11427/43384en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Blake, Gareth AB - The application of machine learning in actuarial science is a rapidly expanding field that bridges traditional actuarial methods with emerging data-driven techniques. This paper examines how machine learning can be used to calculate an insurance company's Solvency Capital Requirement (SCR). Various machine learning models were trained and tested to assess their predictive accuracy for the SCR across different risk scenarios. The findings indicate that machine learning approaches can reliably forecast the SCR, although interpretability challenges must be addressed due to the complex nature of these models. This work contributes to the existing literature on the intersection of traditional actuarial practices and modern machine learning methodologies. DA - 2026 DB - OpenUCT DP - University of Cape Town KW - machine learning KW - solvency capital requirement LK - https://open.uct.ac.za PB - University of Cape Town PY - 2026 T1 - Application of Machine learning Techniques to the Calculation of Solvency Capital Requirements TI - Application of Machine learning Techniques to the Calculation of Solvency Capital Requirements UR - http://hdl.handle.net/11427/43384 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/43384
dc.identifier.vancouvercitationBlake G. Application of Machine learning Techniques to the Calculation of Solvency Capital Requirements. []. University of Cape Town ,Faculty of Commerce ,School of Management Studies, 2026 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/43384en_ZA
dc.language.isoen
dc.language.rfc3066eng
dc.publisher.departmentSchool of Management Studies
dc.publisher.facultyFaculty of Commerce
dc.publisher.institutionUniversity of Cape Town
dc.subjectmachine learning
dc.subjectsolvency capital requirement
dc.titleApplication of Machine learning Techniques to the Calculation of Solvency Capital Requirements
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMCom
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