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