Non Life Insurance Essay
It is also expedient to verify that the selected distribution eectively models the data because anything short of almost accurate could prove detrimental to future predictions. Hogg and Klugman (1984) serves as a standard reference for this subject.
Adeleke and Ibiwoye (2011) used these same concepts to model claim sizes in personal line non- life insurance in Nigeria. They performed analysis of claim size to determine fair premium to ensure that the premium charged to individual members of the pool is equitable. The main lines of insurance discussed were re, motor, property, theft and armed robbery insurance. In the study, they determined appropriate statistical distribution to t claim amounts for the various lines of insurance listed above. The statistical distributions considered for their study were the exponential distribution, the pareto distribution, the gamma distribution, the weibull distribution and the lognormal distribution. These distributions are discussed below with why Adeleke and Ibiwoye
(2011) deemed them suitable for modeling insurance claim amount.
Exponential …show more content…
Adeleke and Ibiwoye (2011) began their analysis by obtaining the descriptive statistics for the claims data for each line of insurance. The descriptive statistics gave more information about which sta- tistical distribution would be more suitable for the data. To give more support to the exploratory methods of tting the data, they proceeded to plot a histogram of the data over a plot of the tted distributioins. The tted distributions were reduced to the Exponential, Lognormal, Weibull and
Gamma distributions. A more classical technique, the chi-squared goodness of t test to examine the goodness of t of these distributions. This test was chosen over because the Kolmogorov-Smirno test and its modication, the Anderson-Darling test, because they are non parametric.
Their results established that a Gamma distribution would be best for the Property, Fire and the
Commercial lines of insurance, lognormal for the Theft and Motor lines, while Weibull would best t the Armed Robbery line of insurance.
In conclusion, this study has exposed how statistical distributions can be used to t real data.
Stakeholders can base on this information obtained from the estimates to forecast claims to