Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.

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ZusammenfassungWährend die Frequenz und die finanziellen Auswirkungen von Cyber-Schäden immer größere Dimensionen annehmen, haben Versicherer das volle Ausmaß dieses Risikos noch nicht verstanden. Für die Versicherungsbranche nimmt der Aspekt des Kumulrisikos eine zentrale Rolle ein. Aus diesem Grund werden die Bedeutung und Besonderheiten des Cyber-Kumulrisikos erarbeitet und der Status quo gemäß der Diskussion in der Literatur sowie der Erfahrungen aus der Praxis analysiert. Abschließend werden die Grenzen der Versicherbarkeit von Cyber-Kumulrisiken diskutiert, wie auch Möglichkeiten des versicherungstechnischen Risikotransfers.

Abstract-Atmospheric emissions such as NO x from ship engines have a drastic impact on the environment. Controlling them is crucial for maintaining a sustainable growth for any logistics company. The Port of Rotterdam (The Netherlands) is using big data analytics to gain actionable insights into these emissions. Our case study deals with the implementation of the emission calculations and reporting implemented in Hadoop. In the analytical setup we introduce the method for estimating emissions based on recorded ship position data and information about its engines. We present a flexible approach that stores intermediate results allowing different levels of aggregation. These levels of aggregations are per geographical area, per grid or for a whole journey attributed to each visited berth. The results are visualized in a Geographical Information System (GIS). The estimated atmospheric emissions also serves as input for the deposition model. We present some selected results of emissions per grid as well as for pre-defined areas. These results are used by the port to make strategic decision. For future work we recommend to also implement the deposition model in Hadoop as this model is also calculative intensive and therefore it currently only accepts aggregated emissions as input, whereby its accuracy is most likely reduced.