Natural disasters and man-made catastrophes are an indispensable part of the existence and development of the human society. The realization of catastrophic risks leads to numerous human casualties, destruction of economic and social infrastructure and environmental degradation. Therefore, the measurement of these risks in order to adequately manage them, is a prerequisite for sustainable development. Measuring catastrophic risks presents a unique challenge in theory and practice due to insufficient information on factors affecting the likelihood of disasters, as the events of low intensity and high frequency. Classic theories and risk measurement methods have proved inadequate regarding timely identification, reliable assessment and efficient management of these risks. Hence, there is a need for the creation of a new scientific paradigm based on stochastic theory, which will enable more precise quantification of catastrophic risks, primarily risks of natural disasters. This paper deals with an analysis of the contribution of digitalization to the possibilities of measuring catastrophic risks. Big Data, Artificial Intelligence and Machine Learning, as the drivers of the fourth industrial revolution, bring multiple possibilities in terms of predicting catastrophic events. After considering theoretical and methodological approaches, a special emphasis will be placed on the potentials for implemeting these technologies and relevant software solutions for measuring catastrophic risks.

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