Abstract: Due to rare occurring of events in insurance companies in Kenya, this has directly affected the industry resulting to huge losses. Further, it has discouraged most insurance companies to offer cover to some listed risks that seem risky and likely to occur causing massive impacts. In particular, the motor cover which out of 47 insurance companies in Kenya, less than a half of this number offer the cover. These huge claims in insurance, finance and even metrological assume distribution with fat tails. This study concentrates on the right tails of the underlying distribution (extremely large observations), and specifically situations when EVT is assumed to be positive. A case study of Kenindia Assurance claims data illustrates how data sets can be analyzed in practice. It is shown to what extent discretion can/should be applied, as well as how different estimators can be used in a complementary fashion to give more insight into the nature of the data and the extreme tail of underlying distribution. The analysis is carried out from the point of raw data, to the construction of tables which can be used directly to gauge the risk of insurance portfolio over a given time frame. Using R, the obtained the mean excess plot was obtained which helped in measuring the shape of the distribution in the tail. The fitted a GPD over a chosen threshold to help us in measuring the shape of the underlying distribution.
Keywords: MODELLING, CLAIMS, EXTREME VALUE THEORY.
Title: MODELLING OF LARGE INSURANCE CLAIMS USING EXTREME VALUE THEORY: A CASE STUDY OF KENINDIA ASSURANCE COMPANY LIMITED MOTOR BUSINESS
Author: SIMON KINYUA WERU, PROF. A. WAITITU
International Journal of Mathematics and Physical Sciences Research
ISSN 2348-5736 (Online)
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