  In this paper, non-life insurance claims were modelled under the three
parameter discrete generalised Pareto distribution. Data from the National
Insurance Commission of Ghana on reported and settled claims were considered
for the period 2012-2016. The maximum likelihood estimation principle was
adopted in fitting the discrete Pareto distribution to the yearly and
aggregated data. The estimation involved two steps. Firstly, the $\mu$ and
$(\mu+1)$ frequency method of \citet{Prieto2014} was modified to suit the
characteristics of the data under study. Secondly, a bootstrap algorithm was
implemented to obtain the standard errors of the estimators of the parameters
of the discrete generalised Pareto distribution. The performance of the
discrete generalised Pareto distribution is compared to the negative binomial
distribution in modelling the non-life insurance claims data using the
information criteria of Akaike and Bayesian. The results show that the discrete
generalised Pareto distribution provides a better fit to the non-life claims
data.
  Keywords: Non-life insurance claims, discrete generalised Pareto
distribution, negative binomial distribution, maximum likelihood estimation,
information criteria.
