Geoscience Reference
In-Depth Information
Chapter 17
The Role of Scientifi c Modelling
and Insurance in Providing Innovative
Solutions for Managing the Risk of Natural
Disasters
Patrick McSharry
Abstract
Scientifi c modelling and forecasting is rapidly gaining momentum as a
way to identify, assess and manage future global risks and extreme events that may
threaten our planet. Inadequate modelling of extreme events, such as the earth-
quakes in Japan and New Zealand, hurricanes in the USA and the fl oods in Thailand,
show that society can no longer afford to assess risk using a retrospective analysis
of historical observations. A pressing need exists for the use of improved forward-
looking risk analyses. This could be achieved by embracing powerful mathematical
modelling and computational simulations. Prospective risk analyses are already
being used to understand, manage risk and cope with uncertainty. Areas of shared
interest between the insurance sector and society are producing innovative forms of
collaboration between re/insurers, governments and the scientifi c community. There
is also an emerging trend of model-based risk assessment by the insurance industry
for decision-making, pricing and product creation that looks set to increase in the
future. These quantitative models can also be used to support policymakers in mak-
ing appropriate investments to reduce risks and provide early warning systems.
Recommendations include: (1) increasing cooperation between government and
industry to better understand risks by constructing open-access models, improving
data quality and embracing forward-looking risk forecasting techniques; (2) provid-
ing national government funding and offi cial development assistance for IT infra-
structure, data collection and independent evaluation of model accuracy and
parametric insurance products; and (3) developing education and training pro-
grammes for risk management and alternative insurance products.
Keywords
Risk • Forecasting • Extremes • Disasters • Insurance • Early warning
systems • Big data • Models • Public-private partnerships
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