The Role of Insurance and Reinsurance in Disaster
Risk Management and Risk Mitigation
David Simmons
Managing Director: Capital, Science and Policy Practice
Introduction
Catastrophe re/insurance is an established, proven product
For example 2011, an “annus horribilis” for Asia/Pacific Catastrophe events
 Christchurch Earthquake(s):
Economic Loss $ 18bn, Insured Loss $14bn
 Bangkok/Thai Flood:
Economic Loss $ 30bn, Insured Loss $12bn
 Japanese Earthquake/Tsunami: Economic Loss $210bn, Insured Loss $35bn
The global reinsurance market for catastrophe risk is strong
 Huge inflows of capital from pension funds (over $50bn) seeking return
 Established reinsurers are seeking new sources of income from markets they
have little current exposure
 Catastrophe Modelling techniques are well established and widely understood
But the benefits are more than just financial restoration
Recent schemes focus upon speedy cash injection to facilitate immediate post-disaster response
 Caribbean Catastrophe Risk Insurance Facility and African Risk Capacity
The value is far more; greater risk awareness, better risk management, planned disaster response
 Incentivisation to improve risk management by pricing mechanisms
 Improved risk understanding from the modelling/product design process
 Leading to better risk management and post loss contingency plans
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National Catastrophe Schemes
Existing Schemes
Most existing schemes protect domestic property
 Catastrophic risks can be too large for local insurers to bear
 A national scheme provides basic property insurance coverage to the population
Typically coverage is for residential property only
 Commercial property generally is covered in the open insurance market
 Governmental property is mostly self insured
With a few exceptions the schemes are indemnity based
 But with limited coverage
 And with features to speed loss settlement
Cover may be compulsory or optional
 Optional schemes risk adverse selection and low take up
 Compulsory schemes may be publically unpopular
Governments may guarantee the fund
 In some schemes the government is explicitly reinsurer of last resort
 In other cases benefits reduce if the scheme exhausts its fund after a major loss
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National Catastrophe Schemes
Key
Features
Scheme
characteristics
Coverage in schemes is typically limited
 With insured able to “top-up” cover from local insurers
Rates may be flat, appealing to concepts of national solidarity, or risk adjusted
 Risk adjusted best influences behaviour and is required for international schemes
A few schemes give immediate post-loss funds to governments to mitigate losses / speed
recovery
 eg Fonden in Mexico, CCRIF in the Caribbean, PCRIP in the Pacific and ARC in Africa
A few schemes operate on a regional rather than national level
 CCRIF, PCRIP, ARC and Europa Re
One scheme explicitly provides cover to local government and infrastructure exposures
 Fonden
Almost all schemes depend upon global reinsurance markets
 Exceptions: CCR in France, CCS in Spain and JER in Japan
All schemes buying reinsurance use reinsurance brokers bar one, PCRIP
 Reinsurance brokers help design, model and place the optimal reinsurance at best possible
terms
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National Catastrophe Schemes
Regional,
National,
Structure and
DesignSub-National
Most existing schemes are national
 Largely covering domestic property
 Few, if any, covering state assets
Fonden is an example of a sub-national scheme
 Fonden provides cover to federal agencies and state governments in Mexico
 Other such schemes have been proposed (eg China), there are no technical barriers at all
Regional Schemes are growing
 CCRIF was the first in 2007
 Followed by Europa Re in 2010, PCRIP in 2013 , ARC in 2014, CCRIF Central America 2015
Regional schemes are harder to put in place
 Must be fair to all parties, no hidden cross-subsidisation
 Need a strong sponsor(s); eg the World Bank and CARICOM for CCRIF
The modelling underpinning a regional or national scheme can support local initiatives
 CCRIF, directly or indirectly, has spawned a number of micro insurance initiatives
 Similarly modelling undertaken for, say, a cities/municipalities scheme can be the basis for a later
national and/or series of local schemes or vice versa
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Structure and Design
Insurance
andself-insured
Reinsurance
Is the scheme
or protected?
Some schemes do not buy reinsurance
 eg CCR in France and JER in Japan
 The state acting as reinsurer of last resort and/or providing loan facilities to the scheme
But the vast majority of schemes however protect themselves with reinsurance
 Arranged by a reinsurance broker to ensure best structure at best price
 Reinsurers are showing great flexibility, eg offering multi-year deals to lock in price and capacity postloss
There is increasing interest in use of the capital markets
 For example the World Bank arranged Catastrophe bond for CCRIF announced on 27th June
(replacing an early catastrophe swap arrangement)
 Capital market prices are becoming increasingly competitive, although for the recent African Risk
Capacity placement, they could not quite compete on price
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National Catastrophe Schemes
A
potential
scheme
Structure
and
Design:structure
Example
Management Board
Policyholder
Representatives of Local
Government, National
Government, Local Insurance,
Experts
of parametric
insurance policies
City/Municipalit
y
City/Municipalit
y
City/Municipalit
y
Risk
Monitoring
and
Evaluation
Trigger + State of
emergency
declaration
Insurance payout
Scheme built around a
Catastrophe Fund
Reinsurance
premium
Trigger payouts
Reinsurance
Capacity
Premium
City/Municipalit
y
Local and National
Government
Premium
External
Donors
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Structure and Design
Or
Something
in Between
Parametric
vs Indemnity
Complexity
Indemnity
Parametric
•Scheme pays on actual loss
•No basis risk
•But high cost of loss adjustment
•Loss adjustment also results in payment delays
•An event occurs, payment is made
•Simple, easy to understand
•Event definition made by a verifiable independent agency (eg USGS)
•But high basis risk: smaller events may cause a large loss, a large
event conversely may cause few losses
Basis Risk
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Structure and Design
Or
Something
in Between
Parametric
vs Indemnity
Complexity
Indemnity
Modelled Loss
Basis
•Scheme pays on actual loss
•No basis risk
•But high cost of loss adjustment
•Loss adjustment also results in payment delays
•Model pays based on estimated loss from a catastrophe model
•Basis risk should be low but still real
•Requires time and expense to build the catastrophe model
•Catastrophe models are good for homogenous exposures (ie
domestic property), less good for complex risks
Parametric
Index
•Essentially a simplified version of a modelled loss
•Formulae estimate hazard at certain reference points (eg wind speed,
ground shaking, rainfall)
•Additional formula estimate the loss resulting from this hazard
•Lower basis risk than pure parametric, but higher than Modelled Loss
Parametric
•An event occurs, payment is made
•Simple, easy to understand
•Event definition made by a verifiable independent agency (eg USGS)
•But high basis risk: smaller events may cause a large loss, a large
event conversely may cause few losses
Basis Risk
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Structure and Design
Parametric vs Indemnity
Parametric or Indemnity (or both?)
It is important to decide what is most important
 Speed : both speed of payment or speed of set-up
 Basis risk: ie accuracy of loss payment
If speed of payment is important, a form of parametric cover makes sense
 Both CCRIF and ARC promise payment within days of an ‘event’ occurring
 Simplified policy terms can speed up indemnity payments but the chance of delay and dispute
remains
If avoiding basis risk is important, indemnity or near indemnity is required
 Individuals cannot be exposed to basis risk, at micro level risk too high and tolerance low
 But a fund may consider accepting some low level basis risk as a trade against speed of settlement
It is possible to migrate
 CCRIF started with a parametric index, allowing the scheme to be up and in place quickly
 After 3 years, and after more research/model building, they switched to a Modelled Loss basis
It is also possible to have both
 Fonden scheme has a parametric catastrophe bond element to provide immediate post-disaster
funding
 And also an indemnity based insurance to cover local governmental assets
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Structure and Design
a
hybrid scheme
Example
of a hybrid scheme: Fonden
Road and
Bridges
Deductible
Hospitals
Deductible
Water
infrastructure
Schools
Special Vehicle (Fonden)
Deductible
Deductible
Trigger product
Indemnification
insurance
Fast Payout
Capable of near immediate implementation
Cost of reconstruction and repair
Mid-/long-term development perspective
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Structure and Design
What
is covered?
Coverage
Most national schemes cover private residential property only
 Relatively homogenous
 Well modelled by most catastrophe models
 Relatively simple to rate
Most also cover only a proportion of the value of the property
 Limits scale of scheme
 Provides some flexibility around loss settlement, to speed and simplify process
 Provides room for local insurers to provide top-up covers
Other risks provide modelling challenges
 Law of large numbers doesn’t apply
 Varied building types, building standards, occupation, stock and machinery values, business interruption
 Governmental assets often a particular issue; often there is no directory of risks, especially infrastructure
Some schemes provide immediate disaster response
 Very difficult/impossible to assess accurately loss of governmental income/additional costs post disaster
 But relatively simple to define what level of response would be useful post an event
 Schemes such as CCRIF only provide a fraction of total response requirement : it’s all about speed
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Structure and Design
Compulsory
Optional
Compulsory ororVoluntary
Many schemes are compulsory
 Where there is a requirement to minimise call on central governmental funds
 Where the scheme also wishes to encourage appropriate risk management behaviour
Where there is an established insurance market this is easier
 For example where there is already high insurance penetration for fire, catastrophe can piggy-back
 It is tougher where there is little insurance currently bought
Take-up is not solely driven by wealth
 For example, take up for the Californian Earthquake Authority scheme is low in some wealthy areas
 But compulsion is clearly more difficult where the population has little, if any, surplus income
Compulsion can be enforced in many ways
 eg required to get a mortgage/property loan, required to purchase power from utility company
 But any compulsion is politically unpopular
Without compulsion schemes can be unviable
 Adverse selection: only those particularly at risk buy the cover
 Unless premiums are risk rated, the scheme’s finances may not add up
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Structure and Design
Price
Setting or Risk Rated?
Flat Premium
The key decision is whether the scheme is risk rated or not
 Risk Rated:
Premium depends on the relative assessment of risk that the policy adds to the scheme
 Flat Premium: All pay the same premium rate regardless of their risk level
Advantages of Risk Rated
 No cross–subsidisation, transparently fair to all policyholders (if the underlying modelling is seen to be
fair)
 Mechanisms can be built in to encourage active risk management, eg lower premiums if roof ties fitted
Advantages of Flat Premium Rate
 Simple
 Consistent with concept of national solidarity
In practice hybrids are often adopted
 A maximum rate may be applied regardless of risk as politically unacceptable otherwise
 Encouragement to manage risk can still be built into the rating system
Whatever is adopted, care must be taken that the scheme understands its own risk
 Set overall premium levels as a multiple of modelled annual expected losses
 Allow for costs of administration, reinsurance and ideally have some left to let scheme funds grow in
good years in order to have more money to pay out in bad years.
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Data and Modelling Requirement
Basic
Principles
Modelling
Process
The modelling process is split as follows:
1. Hazard:
The event: hurricane, earthquake, flood, drought etc
2. Attenuation:
How this impacts risks at different locations – ie maximum wind speed,
ground shaking, water coverage, lack of rain
3. Exposure:
What is there to be damaged, what are its features (eg how built, how high)
4. Vulnerability:
How the property reacts to the wind speed or ground shaking or depth of
water, what loss arises
5. Financial:
How this translates to a claim via the terms of the insurance policy
The data and modelling requirements vary with the type of policy
 Indemnity :
1 to 5 required
 Modelled Risk:
1 to 5 required
 Parametric Index: 1 plus simplified 2 to 5 required
 Parametric:
1 plus very simplified 2 to 5 required
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Data and Modelling Requirement
Is
Good
Data a Pre-Requisite?
Data
Requirement
It is easy to believe that these schemes cannot go live without excellent data
 It is undoubtedly true that the better the data, the better the product
 Good data also makes the product more attractive to reinsurers
 But it is also true that data is never perfect, to wait for perfection implies a very long wait
 It is possible to put a scheme in place quickly with imperfect data, refining it as data (and models
reflecting and using that data) become available
Parametric products can provide real, immediate value without the same data requirements
 Data for wind and rainfall, both live and historical, are usually available from reputable sources
 A Parametric Index product based upon these data could be put in place immediately
 Attractive to reinsurers as transparent and based upon excellent, homogenous hazard data
The product can be refined as data becomes available
 Like CCRIF, move from a Parametric Index to a Modelled Loss basis as modelling becomes available
 CCRIF has not moved to a full indemnity basis as it wants the fast payment that a Modelled Loss
basis brings
 Similarly ARC will continue to use a Parametric Index basis as fast payment is fundamental to its
value
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Data and Modelling Requirement
Example:
PRISM
Proposed Philippines
municipality scheme example (PRISM)
The exceedance of a defined rainfall amount or wind speed triggers a payment
The payment is based on a calculated return period
1. Return period
calculation
per LGU*
2. Actual event
monitoring
3. Trigger payment
per LGU*
Categorization
of intensity
Real time monitoring (meteorology)
 3 hourly rainfall (daily
aggregation)
 6 hourly wind speed (daily max)
*PRISM concept suggests payout as percentage of LGU (= local government unit) tax income class and based on state of calamity declaration
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Reinsurance/Risk Mitigation
General
Principles
Why these
schemes work fior re/insurers
Reinsurers (and capital markets) like
 Transparency
 Verifiable modelling
 Consistency of relationship/trust
Reinsurers do not like
 Political risk
 Poor, incomplete or misleading modelling
 Uncertainty (around what they are covering)
Different reinsurers have different appetites
 Some like writing risks with a lot of premium but relatively high risk
 Others like writing risks with less risk but commensurately less premium
 It is important to know the market and split up the reinsurance placement to appeal to all appetites
But all have appetite for non-correlating exposure
 $1 of exposure in Florida may require 80¢ of capital to write as their capital is driven by hurricane risk
 $1 of exposure in SE Asia may require 10¢ of capital as reinsurers have relatively little existing
exposure
 This should result in low technical pricing for SE Asia catastrophe business assuming risk assessment
is trusted
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Reinsurance Risk Appetite
Example:
African Risk
Capacity
How has reinsurance
appetite
changed over the last 8 years?
It is also interesting to compare to the experience of trying to place the African Risk Capacity for
the first time in 2014 compared to the experience of CCRIF when it launched 7 years earlier
African Risk Capacity
CCRIF
Interest from 25+ reinsurers and
funds-backed markets
Interest from circa 8 reinsurers
15 quotations received
5 quotations received
12 reinsurers on contract
3 reinsurers on the contract
Very keen price
Very keen price
ARC “flew off the shelves” despite a contract structure that was tough to understand, unknown
modelling and a price as cheap as chips.
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Africa Risk Capacity
Introduction to African Risk Capacity (ARC) and ARC Ltd
 ARC is a sovereign risk pool designed to
provide immediate financial response if
there is a drought
 For 2014/15, the pool covered 4
countries, for 2015/18 eight but the aim is
to increase this to 20+ within 4 years
 Cover is triggered by a parametric index
developed with the World Food
Programme based on staple crop rainfall
requirements
 Rainfall is measured by a network of
satellites at a 10km x 10km square
resolution
ARC Members as at March
2015 (Mali joined in April)
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AFRICA Risk Capacity
Africa Risk View – sample output
© 2016 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.
21
Covered Countries 2015-16
Different growing seasons assist diversification
Growing Season are spread throughout the year driving another element of diversification even for
adjacent countries (eg Senegal and The Gambia)
May
Jun
Jul
2015
Aug
Sep
Oct
Nov
Dec
Jan
Feb
2016
Mar
April
May
Jun
Niger
Mali
Senegal
Burkina Faso
Gambia
Mauritania
Kenya EAR2 Arid
Kenya EAR2 Semi-Arid
Malawi
Zimbabwe
Kenya EAR1 Arid
Kenya EAR1 Semi-Arid
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Africa Risk View
Customisation of ARC coverage for each member country
Determine Africa RiskView (ARV) parameters
 The basis of their parametric index, set with ARC staff
Calculate Water Requirement Satisfaction Index (WRSI), based on:
 ARV settings and rainfall measurements from satellites using NOAA
(the US Weather Agency) algorithms
 The staple crop currently grown, recent climatic condition and farming
practice
Determine a WRSI benchmark
 Reflect recent and expected conditions
 Based upon historical as-if losses and local experience
Set drought thresholds as a percentage of WRSI benchmark
 Thresholds represent severe, medium and mild drought
Estimate the proportion of the population that would be affected by
severe, medium and mild droughts
 Based upon population surveys
Set a response cost per affected person
 Corresponds to their budgeted contingency plans
10-day rainfall imagery at 10x10 km
resolution across Africa.
1 May 2015 – 10 January 2016 comparison
to normal
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As-if Historical Losses: Response Costs
Annual Response Costs per Country/Season (USDm) before insurance
 The 2015 national level response cost is
calculated by country/season using the
vulnerability-area level data from ARV
 This data includes regression analysis that
extends the vulnerability area level data
back to 1983
 A clear downward trend can be seen
reflecting:
 Wetter conditions in most territories /
seasons
 The unwinding of conservative forward
calculation of means and median for
benchmark calculation
 ARC Ltd pay-outs are then calculated by
applying the insurance policy information
*2014 assumes no further losses
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Understanding benchmark revaluation: example Niger
The historical set is consistent with science and other observation
 The rising trend in rainfall, reflected in the WRSI
benchmark, is clearly seen across Niger in the
1990s
 The benchmark transitions into a more stable
phase since 2000
 The distorting impact of the forward mean until
1993 is clearly seen
 Records from the Niger national meteorological
services shows that the drier period extended
into the 1970s
 The assumptions made in the historical dataset
are conservative
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African Risk Capacity
Risk Management Benefits
Countries spend up to a year working with ARC experts to understand the drought risk in their
countries:
 ie what crops are grown, where, what rainfall requirements do they have, what defines crop failure,
how many would be affected, how much immediate aid per person is required
Countries are also required to draft a Disaster Response Plan
 Outlining how insurance recovery will be spent to maximise impact; eg buy grain on international
markets, move grain from central stores, move water to feed livestock, provide alternative seed-crop
to salvage growing season etc etc
They must also obtain a Certificated of Good Standing
 To demonstrate good financial management so money’s received will be put to appropriate use
Post loss they must further provide a Final Implementation Plan
 Refining the draft Disaster Response Plan to determine a precise plan of action to maximise the
societal benefit of the cash received
The process of buying cover is arguably as important as the cover itself
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African Risk Capacity
Why
ARC work?
Why itdoes
works
• Access to a $100m+ pot of donor funds
• Assistance to develop risk understanding and develop disaster risk plans
Governments • Valuable insurance cover at a low price
Donors
Reinsurers/
Capital
Markets
• Low political risk
• Private/public partnership, ticks all boxes
• Developing risk awareness, improving disaster response
• Low political risk
• New risks from under-represented countries, largely uncorrelated with
their existing business
• Transparent contract terms
Everybody’s objectives are satisfied!
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Conclusion
Re/insurance provides real benefits to developing countries
Global re/insurance markets will actively support developing country catastrophe schemes
 We have not just financial capacity, but modelling and structuring expertise to share
There are a variety of existing templates to consider
 But the scheme each city/regional scheme must be designed to suit their specific requirements
Certain features attract both reinsurance markets and donor capital
 Transparency is the key
Data and modelling are important but there is no need to wait for perfection
 Valuable schemes can be put in place immediately as long as the data they do use is well founded
The scheme can grow and develop over time
 Like CCRIF, can move to a more technical trigger as enhanced data and modelling become available
 Or like Fonden, with both parametric and indemnity elements
Schemes can be constructed to encourage transfer or risk management expertise
 ARC is a great example of the possible
Reinsurers have a big appetite for developing market catastrophe risk
 The price must be technically credible: the higher the uncertainty, the higher the price
 But SE Asian risk is diversifying, capital charges loads should be low making cover affordable
We open to the floor for questions and comments
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The Role of Insurance and Reinsurance
in Disaster Risk Management and Risk
Mitigation
David Simmons
Managing Director: Capital, Science and Policy Practice
David Simmons: Managing Director, Analytics
+44 7961160906
Email:
Phone:
Mobile:
[email protected]
+44 20 3124 8917
+44 7947 383777
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