5th International Disaster and Risk Conference IDRC 2014 Integrative Risk Management - The role of science, technology & practice 24-28 August 2014 in Davos, Switzerland

1. How has Catastrophe Risk Modelling,
Capital Management and Regulation
enhanced Reinsurers’ Resilience to Natural
Disaster Risk over the last 25 Years.
IDRC Davos 2014
25 August 2014
Esther Baur

2. The challenges: managing rising natural catastrophe
insurance losses …
IDRC Davos 2014 | Esther Baur
2
Insured losses

3. … and developing new insurance solutions to
address the protection gap
IDRC Davos 2014 | Esther Baur
Economic vs insured losses
3
Source: sigma 1/2014

4. How have re/insurers responded to rising natural
catastrophe losses over the past 25 years?
• From historic risk assessment to probabilistic risk
modelling
• From "silos" to integrated risk management
• From simple capital adequacy ratios to sophisticated risk
and capital modelling and stress testing
• New risk transfer mechanisms (swaps, cat bonds, etc)
• Emergence of the Chief Risk Officer
IDRC Davos 2014 | Esther Baur
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5. Illustration of history of natural catastrophe
assessment/modelling
IDRC Davos 2014 | Esther Baur
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Historic
analysis,
actuarial
models,
burning costs,
extrapolations,
typically based
on Pareto
Paper-based
models for
different
exposures of
portfolios,
deductibles of
insureds)
Scenarios to
calculate as-if
losses
First
probabilistic
models
First IPCC
report
Fully
probabilistic
natural
catastrophe
models for all
major perils
Prior to 1980s 1980s: large
storm losses
1990s: large
storm losses
Since 2000s Ongoing/Future
Open source
models
Common data
standards
Usage outside
re/insurance
(private and
public sector)

6. IDRC Davos 2014 | Esther Baur
Four components
of a natural catastrophe model
What is covered?
Where? How?
Risk Loss resistance
Value
distribution
Coverage
conditions
 Insurance sums
 Limits
 Excess
 Exclusions
 etc.
How often?
How strong?
Example
Hurricane
“Charley”
Aug 2004
How well built
and protected?
Natural catastrophe models integrate scientific, engineering,
geographic and financial perspectives.

7. IDRC Davos 2014 | Esther Baur
Example:
Probabilistic hurricane modelling
historic
~100 years
probabilistic
~10‘000 years

8. Risk and capital management: stress tests to quantify
potential impact of risk exposures
IDRC Davos 2014 | Esther Baur
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Swiss Re Annual Report 2013

9. Fully integrated risk and capital management: Group
capital requirements based on 99% Tail VaR
IDRC Davos 2014 | Esther Baur
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Swiss Re Annual Report 2013

10. Study suggests ERM
adoption reduces cost
2006 2014
GSII
Reinsurers
GSII
Insurers
of capital1
2013
US treasury
creates CRO role
2012
New York appoints
NY2100 Commission
after Hurricane Sandy;
recommendations
include appointing
CRO for NY state
IDRC Davos 2014 | Esther Baur
Evolution of the Chief Risk Officer
GE Capital appoints
James Law head Credit,
Market & Liquidity risk:
calls himself "CRO"
Public Sector Private Sector
1993
S&P / AM Best disclosures
establish link between
ERM and Financial
Strength Ratings
Economist survey
shows ~45% of
companies have a CRO
(61% in Financial
2002
Sarbanes
Oxley
Services)
2004
Basel II
2005 2005-6
Lehman
Brothers
files for
bankruptcy
2009
Solvency II
directive
2010
Basel III
2008
Mexico becomes first
country to transfer risk
to markets with
securitised insurance
Swiss Re
appoints its
first CRO
1997
Euro
storms:
Lothar,
Martin
1999
CRO a "recent" development, embraced by enterprise and, increasingly, the public sector
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11. • Probabilistic risk models can also be used by other industries and
public sector to assess and address risks (Example: ECA studies,
Government of Mexico).
• Natural catastrophe risks represent contingent liabilities on balance
sheets of private and public sector, which can be quantified.
• Capital requirements/reserves for contingent liabilities for all
industries and the public sector would provide incentives for
investments in prevention and risk transfer.
• A Country Risk Officer (similar to the Chief Risk Officer in the private
sector) could greatly facilitate integrated risk management – across
all risks and from prevention to risk transfer – in the public sector.
IDRC Davos 2014 | Esther Baur
Conclusion
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12. IDRC Davos 2014 | Esther Baur
Appendix
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13. What is covered?
Where? How?
IDRC Davos 2014 | Esther Baur
Data sources for
natural catastrophe models
Risk Loss resistance
Value
distribution
Coverage
conditions
Exposure data from
our clients
How often?
How strong?
How well built
and protected?
In our models we combine data from government agencies
with our own rich claims experience.
National weather
and earthquake
services
Published scientific
papers
Claims experience
of Swiss Re
Published scientific
papers
Exposure data from
our clients.
Public data sources
(e.g. population
density)

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Integrated, quantitative risk and capital
management

15. IDRC Davos 2014 | Esther Baur
Definition of risk tolerance
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17. IDRC Davos 2014 | Esther Baur
Legal notice
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