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|Title:||An analysis of claim frequency and claim severity for third party motor insurance using Monte Carlo simulation techniques|
|Keywords:||regression;MCMC;Gibbs Sampler;logistic;poisson;negative binomial;zero-inflated;insurance;claim frequency;claim severity;expected compensation|
|Abstract:||The purpose of this thesis is to introduce the reader to Multiple Regression and Monte Carlo simulation techniques in order to find the expected compensation cost the insurance company needs to pay due to claims made. With a fundamental understanding of probability theory, we can advance to Markov chain theory and Monte Carlo Markov Chains (MCMC). In the insurance field, in particular non-life insurance, expected compensation is very important to calculate the average cost of each claim. Applying Markov models, simulations will be run in order to predict claim frequency and claim severity. A variety of models will be implemented to compute claim frequency. These claim frequency results, along with the claim severity results, will then be used to compute an expected compensation for third party auto insurance claims. Multiple models are tested and compared.|
|Appears in Collections:||Computational Sciences - Master's theses|
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