Claims experience in non-life insurance is contingent on random eventualities of claim frequency and claim severity. By design, a single policy may possibly incur more than one claim such that the total number of claims as well as the total size of claims due on any given portfolio is unpredictable. For insurers to be able to settle claims that may occur from existing portfolios of policies at some future time periods, it is imperative that they adequately model historical and current data on claims experience; this can be used to project the expected future claims experience and setting sufficient reserves. Non-life insurance companies are often faced with two challenges when modeling claims data; selecting appropriate statistical distributions for claims data and establishing how well the selected statistical distributions fit the claims data. Accurate evaluation of claim frequency and claim severity plays a critical role in determining: An adequate premium loading factor, required reserve levels, product profitability and the impact of policy modifications. Whilst the assessment of insurers' actuarial risks in respect of their solvency status is a complex process, the first step toward the solution is the modeling of individual claims frequency and severity. This paper presents a methodical framework for choosing a suitable probability model that best describes automobile claim frequency and loss severity as well as their application in risk management. Selected statistical distributions are fitted to historical automobile claims data and parameters estimated using the maximum likelihood method. The Chi-square test is used to check the goodness-of-fit for claim frequency distributions whereas the Kolmogorov-Smirnov and Anderson-Darling tests are applied to claim severity distributions. The Akaike information criterion (AIC) is used to choose between competing distributions. Empirical results indicate that claim severity data is better modeled using heavy-tailed and skewed distributions. The lognormal distribution is selected as the best distribution to model the claim size while negative binomial and geometric disHow to cite this paper: