Includes abstract. / Includes bibliographical references (p. 39-41). / 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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/5872 |
| Date | January 2009 |
| Creators | Smith, Bradley |
| Contributors | MacDonald, lain |
| Publisher | University of Cape Town, Faculty of Commerce, School of Management Studies |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MBusSc |
| Format | application/pdf |
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