Comparison between parametric and non-parametric methods of estimating comprehensive motor claim severity distributions
Date
2020,
Authors
Kimani-, Ruth Wangari
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Claim severity is the amount of loss associated with an insurance claim. Insurers
compensate policyholders who have suffered a loss from the occurrence of an insured
risk. Insurance companies have been estimating claim severity by using normal
distribution meaning; they assume an average cost of motor claims to estimate the total
claims amount. However, this method is not very efficient because not all motor claims
follow a normal distribution. To deal with this, there has been an introduction to using
other parametric distributions such as the gamma and log-normal distribution.
Parametric distributions do not consider the outlier claims that do not follow any of the
parametric distributions and this is what led to using non-parametric distributions. The
data used in this research study consisted of an auto-insurance portfolio of a company
operating in Sweden, which was compiled by the Swedish Committee on the Analysis
of Risk Premium in Motor Insurance, (Hallin & Ingenbleek, 1983). The motor insurance
data is cross sectional and it involves the third-party liability auto-insurance claims for
the year 1977. The only variable I worked with were the claim amounts. The main aim
of this research study was to employ both parametric and non-parametric models in
estimating the claim size distribution. From the data analysis that was carried out, it can
be concluded that the non-parametric method is the most suitable one for estimating
motor claims severity distributions .
Description
Submitted in partial fulfillment of the requirements for the Degree of Bachelor's in business science in Actuarial Science at Strathmore University