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dc.contributor.authorNduwayezu, Florent
dc.date.accessioned2017-09-01T07:39:45Z
dc.date.available2017-09-01T07:39:45Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/11071/5361
dc.descriptionA Research project submitted in partial fulfillment of the requirements for the degree of Bachelor of Business Science in Actuarial Science at Strathmore Universityen_US
dc.description.abstractObtaining the total amount of claims for a specific period is a vital part of the daily work of insurance companies. This will help in various ways the management in running the company (Jouravlev, 2009). For instance, the insurance company will be able to calculate the premium for a type of policy by the use of the claim experience. Moreover, it will be able to reserve a certain amount of money to cover the cost of future claims. Premium computation and Reserving are not the only reasons for which loss distributions are needed. Loss distributions are also utilised in reviewing reinsurance arrangements and also in testing for solvency. This explicitly highlights the importance of loss distribution in the insurance industry. This paper therefore aims to determine the most suitable loss distributions for various sort of insurance contracts being general or life insurance in the Kenyan market industry. The following distributions will be compared: the exponential distribution, the Pareto distribution, the Generalised Pareto distribution, the lognormal distribution, the Weibull distribution & the Burr distribution. We will see how these distributions can be tailored in order to suit the observed data. Afterwards, a test of goodness-of-fit will be used to determine the level of robustness of the distribution in fitting the given data. The loss distributions will also be used in order the probabilities of future events happening.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectInsurance dataen_US
dc.subjectLoss distributionsen_US
dc.subjectExponential distributionen_US
dc.subjectPareto distributionen_US
dc.titleFinding appropriate loss distributions to insurance data Case study of Kenya (2010-2014)en_US
dc.typeProjecten_US


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