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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/9991

Title: Predictive Modeling of Insurance Claims Using Reversible Jump Markov Chain Monte Carlo Methods
Authors: Duah, Collins Afranie
Issue Date: 19-Jan-2017
Abstract: There has been considerable amount of attention rendered to claims reserving methods over the last few decades in actuarial science. The commonly used method of estimating claims reserves is the chain ladder technique. The underlying principle of the chain-ladder technique is that no underlying pattern to the run-o , and that each development year should be allocated a separate parameter. Applicable to a wide range of data, the chain ladder could alternatively be condemned for having too many parameters and also assumptions have to be used to estimate reserves beyond the latest development year already observed. This research seeks to explain an approach to model the development of claims run-o , using reversible jump Markov Chain Monte Carlo (RJMCMC) method. The study uses claims data from a renowed Insurance Company in Ghana; Win- BUGS the tool used in simulating the reversible jump Markov Chain Monte Carlo (RJMCMC) method. The Bayesian methods are found to be better than the Over-dispersed Poisson model with lower predictive errors.
Description: A Thesis submitted to the Institute of Distance Learning, Department Of Mathematics, Kwame Nkrumah University Of Science and Technology in partial fulfillment of the requirements for the Degree of M.Sc. Actuarial Science, 2016
URI: http://hdl.handle.net/123456789/9991
Appears in Collections:Distance Learning

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