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    A study on motor claim/reserving and premium rating using run-off triangle and emperical bayes credibility theory

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    Date
    2003
    Author
    M'thambura, G K Emenyi
    Type
    Thesis
    Language
    en
    Metadata
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    Abstract
    This is a study on the wrangles between motor insurance consumer and especially the 'matatu' owners and the association of Kenya Insurers. The study examines the fundamentals of motor insurance premium and goes further to examine the probable cause of collapse of some insurance companies underwriting motor due to poor reserving techniques and premium fixing. The work has been split into six chapters. The first section comprises the introductory chapter covering the general introduction formalities of the study. Chapter one sets the premise against which the inadequacy of proper data storage is examined. It sets the problem statement, the hypothesis, the objectives of the study, the assumptions, justifications of the study, the scope and limitations, research methodology and operational definition of terminologies. Chapter two consists of the reviewed literature which outlines the source of data and target players in the industry for data capture. It does also explain the difficulties encountered during data collection and data limitations associated with record keeping. Chapter three consists of data analysis using run off triangle and creation of reserves. It goes further into explaining the model of data to estirgate-outstanding Chapter four details the prermum pricing using, Bayes Empirical Credibility Theory Model. Chapter five shows the summary of results from data analysis, conclusions and recommendations. Chapter six consists the related information sources consulted like Text books, journals and others.
    URI
    http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/23939
    Citation
    PGD- Actuarial Science
    Sponsorhip
    University of Nairobi
    Publisher
    School of Mathematics, University of Nairobi
    Description
    Master of Nairobi
    Collections
    • Faculty of Science & Technology (FST) [4213]

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