A major problem within the insurance industry is the differentiating between high and low risk policyholders as well as high and low risk areas when determining the premiums. The purpose of this thesis is to investigate if there exists spatial dependency in claim frequencies and between different types of insurance classes. This thesis uses four different types of processes (Multi Strauss Hard, Pariwise, Strauss, Penttinen) to measure the interaction between insurance classes in order to research if spatial dependencies exists. Furthermore, the author investigates if the spatial dependencies points to spatial clustering or inhibition. Moreover, the author tries to explain what a potential spatial clustering between the two insurance classes’ burglaries and bike theft might indicate. The findings show that there exists spatial dependencies between different insurance classes on a radius of 60 meters and that the interaction show spatial clustering. Furthermore, the findings show that if there is presence of burglaries there is high probability that bike thefts will occur in a radius of 60 meters. Insurance companies can use these findings to further investigate dependencies between different insurance classes in an effort to detect fraud and adjust premiums to match the risk exposure in different areas.