A comparison of methods for modelling rates of withdrawal from insurance contracts

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dc.contributor.advisorMacDonald, lainen_ZA
dc.contributor.authorSmith, Bradleyen_ZA
dc.date.accessioned2014-07-31T12:36:43Z
dc.date.available2014-07-31T12:36:43Z
dc.date.issued2009en_ZA
dc.descriptionIncludes abstract.
dc.descriptionIncludes bibliographical references (p. 39-41).
dc.description.abstractWithdrawal from insurance contracts can be a significant risk for insurers. Withdrawal rates can be difficult to predict because withdrawal is influenced by a number of inter-related factors related to, inter alia, the sales process, characteristics of the insurance contract, characteristics of the contract holder, and economic variables. Existing methods used to model and predict withdrawal rates are initially reviewed. Two additional methods which have been proposed in the literature as means for modelling insurance risks are neural networks and Bayesian networks. These two methods are utilised in order to build models to compare their predictive ability with a commonly used method for modelling withdrawal rates, namely logistic regression.en_ZA
dc.identifier.apacitationSmith, B. (2009). <i>A comparison of methods for modelling rates of withdrawal from insurance contracts</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,School of Management Studies. Retrieved from http://hdl.handle.net/11427/5872en_ZA
dc.identifier.chicagocitationSmith, Bradley. <i>"A comparison of methods for modelling rates of withdrawal from insurance contracts."</i> Thesis., University of Cape Town ,Faculty of Commerce ,School of Management Studies, 2009. http://hdl.handle.net/11427/5872en_ZA
dc.identifier.citationSmith, B. 2009. A comparison of methods for modelling rates of withdrawal from insurance contracts. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Smith, Bradley AB - Withdrawal from insurance contracts can be a significant risk for insurers. Withdrawal rates can be difficult to predict because withdrawal is influenced by a number of inter-related factors related to, inter alia, the sales process, characteristics of the insurance contract, characteristics of the contract holder, and economic variables. Existing methods used to model and predict withdrawal rates are initially reviewed. Two additional methods which have been proposed in the literature as means for modelling insurance risks are neural networks and Bayesian networks. These two methods are utilised in order to build models to compare their predictive ability with a commonly used method for modelling withdrawal rates, namely logistic regression. DA - 2009 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2009 T1 - A comparison of methods for modelling rates of withdrawal from insurance contracts TI - A comparison of methods for modelling rates of withdrawal from insurance contracts UR - http://hdl.handle.net/11427/5872 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5872
dc.identifier.vancouvercitationSmith B. A comparison of methods for modelling rates of withdrawal from insurance contracts. [Thesis]. University of Cape Town ,Faculty of Commerce ,School of Management Studies, 2009 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5872en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentSchool of Management Studiesen_ZA
dc.publisher.facultyFaculty of Commerceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherZoology and Marine Biologyen_ZA
dc.titleA comparison of methods for modelling rates of withdrawal from insurance contractsen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMBusScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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