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In this paper, non-life insurance claims were modelled under the threeparameter discrete generalised Pareto distribution. Data from the NationalInsurance Commission of Ghana on reported and settled claims were consideredfor the period 2012-2016. The maximum likelihood estimation principle wasadopted in fitting the discrete Pareto distribution to the yearly andaggregated data. The estimation involved two steps. Firstly, the $\mu$ and$(\mu+1)$ frequency method of \citet{Prieto2014} was modified to suit thecharacteristics of the data under study. Secondly, a bootstrap algorithm wasimplemented to obtain the standard errors of the estimators of the parametersof the discrete generalised Pareto distribution. The performance of thediscrete generalised Pareto distribution is compared to the negative binomialdistribution in modelling the non-life insurance claims data using theinformation criteria of Akaike and Bayesian. The results show that the discretegeneralised Pareto distribution provides a better fit to the non-life claimsdata.  Keywords: Non-life insurance claims, discrete generalised Paretodistribution, negative binomial distribution, maximum likelihood estimation,information criteria.

Assessing the Performance of the Discrete Generalised Pareto Distribution in Modelling Non-Life Insurance Claims