Why pandemic models can learn much from cyber risk—and the insurance industry can help

15-12-2020

Why pandemic models can learn much from cyber risk—and the insurance industry can help

As the world attempts to find a way of understanding, managing, and transferring the risk associated with pandemics, much can be learned from the extensive work the insurance industry has done to better handle cyber risk, says a new report by CyberCube, with contributions from Munich Re and Metabiota.

The COVID-19 pandemic has exposed many vulnerabilities in the global economies and changed how we think about systemic risk. But there are other scenarios potentially worse than COVID-19 to consider—and risk models aim to understand and articulate the potential of systemic risk to build economic resilience.

That is the core finding of a new report by CyberCube, a leader in cyber risk analytics for the insurance industry, with comments from experts by Munich Re and pandemic modeling firm Metabiota, which examined the parallels between pandemic and cyber risk modelling to understand common lessons in one field that might be applied to the other.
“In both cyber risk and pandemics, there is a need to consider accumulation risk.” Dr Hjalmar Böhm, Munich Re
The report, called “Viruses, contagion and tail risk: modeling cyber risk in the age of pandemics”, notes that in the last few decades a number of pandemics ranging from Asian ’flu back in 1957 to swine ’flu in 2009 have prompted parts of the insurance industry to use pandemic models in order to understand a range of extreme scenarios which can be planned for and ultimately support the solvency of the industry.

Firstly, life insurers have wanted to understand the scope and scale of significant mortality events in order to set reserves accurately and understand tail exposures. Secondly, there has been increased attention on the potential economic consequences of pandemics, and how these might manifest in property/casualty insurance policies, such as event cancellation, business interruption (BI) and related losses.


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