A resimulation framework for event loss tables based on clustering

Abstract

Catastrophe loss modeling has enormous relevance for various insurance companies due to the huge loss potential. In practice, geophysical-meteorological models are widely used to model these risks. These models are based on the simulation of meteorological and physical parameters that cause natural events and evaluate the corresponding effects on the insured exposure of a certain company. Due to their complexity, these models are often operated by external providers—at least seen from the perspective of a variety of insurance companies. The outputs of these models can be made available, for example, in the form of event loss tables, which contain different statistical characteristics of the simulated events and their caused losses relative to the exposure. The integration of these outputs into the internal risk model framework is fundamental for a consistent treatment of risks within the companies. The main subject of this work is the formulation of a performant resimulation algorithm of given event loss tables, which can be used for this integration task. The newly stated algorithm is based on cluster analysis techniques and represents a time-efficient way to perform sensitivities and scenario analyses.