Stochastic Analysis of General Insurance Claims Using Markov Chain Monte Carlo Method

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October, 2016
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Abstract
Motor insurance companies need to utilize past and current claim amounts in predicting future liabilities. It is therefore necessary that, motor insurance com- panies develop actuarial models using Bayesian and Markov Chain and Monte Carlo procedures. This thesis focuses on developing stochastic models for insur- ance claim amounts using Bayesian statistics. Motor insurance claim numbers usually follow compound Poisson distribution. This research work, however ex- amines the claim amount distributions. The methodology requires intensive use of Bayesian and Markov Chain Monte Carlo techniques through a software called WinBUGS. Two distributions usually tted to claim amounts are examined and the issues encountered when using Bayesian and Markov Chain Monte Carlo techniques in this context are investigated.
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A thesis submitted to the Department of Mathematics, Kwame Nkrumah University of Science and Technology in partial fulfillment of the requirement for the Degree of Master of Science in Actuarial Science.
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