Fitting loss distributions in insurance is sometimes a dilemma: either you get a good fit for the small/medium losses or for the very large losses. To be able to get both at the same time, this paper studies generalisations and extensions of the Pareto model that initially look like, for example, the Lognormal distribution but have a Pareto or GPD tail. We design a classification of such spliced distributions, which embraces and generalises various existing approaches. Special attention is paid to the geometry of distribution functions and to intuitive interpretations of the parameters, which can ease parameter inference from scarce data. The developed framework gives also new insights into the old Riebesell (power curve) exposure rating method.
