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

the premiums that we have received from customers —
these premiums are the first line of defence when paying out
insurance claims. The residual amount, known as the value at
risk, is the minimum regulatory capital that we must hold as
additional assets. We hold at least enough capital to survive
a disastrous year that we might statistically expect to see
only once in every 200 years. We then uplift this minimum
capital further to provide a buffer.
Finally, we model any claims that are larger than the
combined funds of any syndicate to see how the central
fund would be affected. Then we ensure that this fund is
large enough to survive all events with a probability greater
than 0.5%, and add more buffer assets. This combination of
mathematically-modelled amounts buffered with additional
prudence helps us to handle the inevitable uncertainties in
the models.
Lloyd’s sells policies to many different businesses, including
aviation, shipping, energy, property and liability business.
Natural disasters directly affect many of these sectors, but
claims can come from other causes such as litigation or
terrorism. The capital calculation described above has to
consider all those risks, while the modelling approaches vary
depending on the type of business.
The modelling of natural disasters in particular has
become more sophisticated over the past two decades.
Losses were previously estimated using illustrative scenarios,
but this approach suffers from many flaws. The scenarios
may miss important exposures, may not be aligned with
a specific probability of failure, and may overlook certain
risks (such as contingent covers) completely. Today, we use
catastrophe models that take simulated events such as
hurricanes and earthquakes and estimate the damage caused
to a portfolio of properties based on their age and building
characteristics. Multiple disciplines come together to create
these models, including actuaries, engineers and scientists.
Regulators allow these models to be used, but must be
convinced that they are built using sound principles and on
reasonable and justifiable assumptions.
Over the years, these models have incorporated many
features of natural catastrophes. These include issues such
as ‘post-loss amplification’, where shortages of raw building
materials and services (such as plumbers and engineers) can
inflate rebuilding costs. Another feature is ‘contract leakage’,
where judicial decisions grant payments to policyholders
outside of the insurers’ usual interpretation or intentions
of a policy’s terms and conditions. Regulators are keen to
ensure that all material features are included in the models
so that they are ‘complete’.
During a disaster, insurers will observe the catastrophe
and start to gear up their claims operations. Loss adjusters
will get ready to assess the level of claims. Regulators will
want initial estimates of the potential losses, to decide
whether the affected firms have any potential solvency
issues. Initial estimates are usually made using the same
models that set the capital — there is significant uncertainty
in these estimates, but they give a reasonable guide to the
financial cost of the event.
Shortly after a disaster, insurers must set up reserves to
pay the expected claims. At this stage many claims will not
have been notified, so the losses must be estimated. And
because there is always uncertainty about the impact of the
event, Lloyd’s typically takes a scenario approach to assess
the damage. We usually ask for each syndicate to provide
its own estimate of loss, but we also specify at least two
scenarios with consistent assumptions that they must all use.
This ensures a set of results that can be aggregated to form
a consistent whole-market estimate.
Some time after the disaster, the underwriters have to
consider how to incorporate the new information into their
prices and into the next generation of models. In the case of
earthquake insurance, for example, the buffer capital has to
cover very large events. Prices (or ‘premium rates’) have to
cover the long-run average claims, plus expenses, and also
provide a return on the capital held in excess of reserves.
Therefore the premiums must exceed the average losses in
order to be risk-based.
The insurance industry understands clearly that much
uncertainty remains. Many models are based on a few
centuries of data, much of it inferred from proxy sources
rather than being directly observed. We can quantify some
of this uncertainty, though, by incorporating statistical
distributions of the key parameters in to the models.
Notwithstanding their limitations, models have significantly
aided the insurance industry’s decision-making about
inherently uncertain events. They incorporate and naturally
illustrate the range of possible outcomes in a way that
would not be highlighted by single deterministic scenarios.
Model-based methods provide a probability-weighted
approach for decisions and are now embedded within the
regulation of insurance in the United Kingdom. If new EU
‘Solvency II’ regulation comes into force, this approach will
be adopted across the European Union. Indeed, we already
see similar approaches around the world: earthquake
insurance in Canada, for example, is required to meet a 1-in-
500 year test.
The modelling of natural
disasters has become
more sophisticated over
the past two decades.
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