14-1190b-innovation-managing-risk-evidence

transport disruption, wind damage, airport closures, or
health effects), rather than any pre-defined meteorological
thresholds. Although the uncertainty in the weather (and
climate) variables is quantified using probabilistic forecast
systems, uncertainty in the downstream models tends not to
be included in determining these risks. For that to happen,
much more research needs to be done in delivering an endto-end
assessment of uncertainties, and therefore of risk.
THE INDUSTRY PERSPECTIVE
Rowan Douglas (Chairman, Willis Research Network)
As a focal point for risk, it is unsurprising that
the global insurance sector has grappled with
the challenges of understanding and managing
extremes. Indeed, the past quarter of a century provides
an informative journey of how the sector has achieved far
greater resilience to catastrophic risks through the interplay
of reforms in the scientific evaluation, prudential regulation
and capital management of natural disaster risk.
Following a period of unprecedented losses from the
mid-1980s, culminating with Hurricane Andrew (which hit
the United States in 1992), the insurance industry faced a
near-existential crisis. Confidence in the global risk-sharing
mechanism was in structural disarray as firms across private,
public and mutual sectors became insolvent or impaired.
After approximately 300 years of successful operation, the
insurance sector’s modus operandi of relying on historical
experience to evaluate risk was unsustainable.
As a consequence, traditional sources of insurance
capital dried up and insurance capacity retreated: coverage
became unavailable, unaffordable or severely restricted.
But insurance is a necessity — it is a prerequisite for many
forms of economic activity — so there was no shortage of
demand for the situation to be resolved. Over the next five
years, three seemingly unrelated forces converged to create
the conditions for a transformation in how the insurance
sector confronted extreme risk.
The first was the intervention of new ‘smart’ capital. The
shortage of capacity had sharply increased prices and there
was money to be made from underwriting risk, but this new
capital demanded a new approach to risk evaluation, and
to managing risk within more tolerable parameters at an
individual policy and portfolio level.
The second was a quantitative revolution that embraced
the developments in mainstream software and computing,
as well as specialist expertise in emerging software firms
known as catastrophe risk modelling companies. These firms
began to develop robust methodologies to understand the
potential locations and forces of threats such as extreme
windstorms or earthquakes; the locations, characteristics
and vulnerabilities of exposed buildings; and potential
financial losses.
The third force was a regulatory trend. An insurance
contract is a promise to pay money when a defined loss
event occurs, but if there is no money to pay a claim the
written promise is worthless. Until the mid-1990s, nobody
had asked what level of tolerance an insurance contract
should be designed to meet, for the simple reason that
in general they had worked well up to that point. Should
contracts operate to the 1-in-100 year risk, or 1-in-1000?
Over a period of approximately five years, an emerging
convention developed among regulators that insurance
contracts should tolerate the 1-in-200 year level of
maximum probable annual loss — that is, to perform at a
99.5% level of confidence. This level meant that insurance
companies should have enough capital to meet all their
policyholder obligations.
There was initially some confusion about what these
terms meant, let alone how to assess risks at these
extreme levels. It presaged a revolution in the industry,
which not everyone was able to adapt to. Slowly but surely,
the techniques and methodologies that were required to
respond to these new demands began to be developed,
applied, tested and refined, and quite quickly the results
began to show.
In 2005, Hurricanes Katrina, Rita and Wilma (KRW) hit
the US Atlantic and Gulf Coasts, causing unparalleled levels
of losses at over US$50 billion. While the insurance and
reinsurance market was put under severe stress, with major
question marks over the accuracy of the modelling these
specific events, there were very few insolvencies. Over the
13 years since Hurricane Andrew, the industry had allocated
a greater proportion of capital against potential natural
disaster loss events, which may have lain beyond previous
underwriting experience. Ultimately there was sufficient
capital in the system. If KRW had hit in 1995 before such
reforms had taken effect, the impact on the sector and
affected populations seeking support would have been
catastrophic.
By 2011, the global industry suffered the worst year
of natural catastrophe losses on record — in excess of
US$120 billion — from seismic events such as the Great
East Japan (Tohoku) and Christchurch earthquakes, and from
weather losses such as the Thailand floods and a severe
US tornado season. Yet despite this, and the wider global
financial crisis, the re/insurance sector carried on almost
unaffected. While there was still much to learn, it had begun
to properly account for risk via the medium of the modelled
world.
Finally, in the aftermath of Super Storm Sandy’s impact
on the New York region in 2012, the confidence in the
modelling and assessment of natural disaster risk liberated
over US$50 billion of new capital to respond to US
disaster risk. Over a period of a quarter of a century, a
new relationship between science, capital and public policy
had delivered a paradigm shift in risk management and
highlighted to many the dangers of overconfidence in past
experience.
The international agenda for 2015 includes the renewal
of the United Nations’ (UN) Hyogo Framework for Action
on disaster risk reduction in March, the UN’s updated
Sustainable Development Goals, and the UN Framework
123