Modelling pandemic and cyber risk hampered by the unpredictability of human behaviour

Modelling human behaviour is one of the biggest hurdles to accurately analysing pandemic and cyber risk, according to a new report from CyberCube.

The report, titled Viruses, contagion and tail-risk: Modeling Cyber Risk In The Age Of Pandemics, aims to better understand what modellers looking at pandemics and cyber risk can learn from each other.

It argued that progress in improving both cyber and pandemic models is being held back by a lack of data. Since the start of the twentieth century, there have been fewer than twelve major global pandemics and relatively few significant systemic cyber events, meaning there is limited data to assess the potential impact of such events.

Addressing current limitations in data collection will improve the value and insight these models can provide to the insurance and life insurance markets, it said.

Although pandemics originate from pathogens, it is the individual and societal reactions to them that are hardest to model, the report noted.

Both cyber risk and pandemics are unencumbered by geographic boundaries and both are heavily influenced by political decisions and the public’s response. Modellers need to understand these influences to accurately forecast the spread of both types of risk, the report argued.

Nita Madhav, chief executive at Metabiota, which provides data, analytics and advice in tackling epidemics, noted that pandemics and cyber risks are network issues. “The impact of mitigation risk and early action can potentially make a difference. Furthermore, you can be asymptomatic with COVID-19; similarly, you may not know if a cyber intruder has already infiltrated your network.”

Hjalmar Böhm, senior actuary in Munich Re’s epidemic risk solutions unit, said both cyber risk and pandemics require consideration of accumulation risk. “A pandemic is a key consideration for life insurers and a high mortality event could create significant economic loss. A solid approach to controlling accumulation risk exposure needs to be the basis for every business model for epidemic risk insurance,” he said.

Oli Brew, CyberCube’s head of client success, said: “It’s clear that lessons can be learnt and applied to cyber risk modeling from understanding how pandemic models have evolved. As the COVID-19 pandemic continues, even though there are differences between computer and human viruses, parallels are emerging in the modeling, the methodologies and the data challenges.”

Brew highlighted the value in learning how to balance the needs of accuracy and precision in developing models among interdisciplinary teams. “At a minimum, the need for a creative, but reality-based imagination to represent forward-looking risks is critical,” he said.