Understanding Natural Hazards And Market Exposure - Natural Catastrophes: Have You Built Your View Of Risk ? - Dr Nicolas Pondard (2014)

1. NATURAL CATASTROPHES:
HAVE YOU BUILT YOUR VIEW OF RISK?
Understanding natural hazards and market exposure
Dr Nicolas Pondard, Divisional Director
25th September 2014

2. Why building your view of risk?
 Probable earthquake near Istanbul: Economic loss USD 40-60
billion (TRY 90-130 billion). 5-10% covered by insurance (Erdik
et al., 2008).
 30 to 40% of the 800,000
buildings damaged
 40,000 fatalities and 120,000
injuries
Izmit earthquake 1999, insured loss USD 800m,
Economic loss USD 10bn (Lloyds, 2011)
(photo courtesy of AykutBarka)
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3. Why building your view of risk?
 Population +4% per year (Sigma, 2013), 70% living in urban
areas
 Inflation +7 to 8% per year
 Insurance penetration 1.5%
countrywide
 Cultural shift: young
population used to insurance,
+15% annual growth
 Compulsory earthquake
insurance (TCIP penetration
27%, Lloyds 2011)
Izmit earthquake 1999, insured loss USD 800m,
Economic loss USD 10bn (Lloyds, 2011)
(photo courtesy of AykutBarka)
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4. Why building your view of risk?
 To assess the likelihood of being insolvent
 To estimate average annual loss and premium
 If the probable maximum loss exceeds capital, need for
appropriate risk management decision
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5. Managing NatCat risk
CAPITAL
MANAGEMENT
PROFITABILITY
REINSURANCE
STRUCTURING &
PLACEMENT
Business
Objectives
REGULATORY
REQUIREMENTS
Own View of Risk Business Applications
PORTFOLIO
MANAGEMENT
REGULATORY
REQUIREMENTS
EXPOSURE
ANALYSIS
RISK
ASSESSMENT

6. View of Risk
Sensitivity
Analysis
Scientific Appraisal – dialogue with model vendors
Hazard model
review
Vulnerability
model review
OWN VIEW
OF RISK
MODEL EVALUATION AND COMPARISON
Developing your Own View of Risk
Validation and
back-testing
Evaluation
Model
Adjustments
and blending
Willis Research Network

7. Risk Assessment
 Example: Average annual loss from AIR and RMS
 2 different approaches providing 2 different answers
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8. Validation – Sensitivity analysis
 AIR versus RMS losses: differences up to 100%
 Understand model assumptions and limitations
8

9. Validation – Sensitivity analysis
 Identify which approach is most appropriate
 Use claims experience and latest scientific research
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10.  Earthquakes are not random
 North Anatolian Fault: succession of active and quiet periods
10
Understanding Natural Hazards
(Pondard et al., 2007)

11. Understanding Natural Hazards
 Likely scenario? Magnitude Mw 7.2 to 7.5 close to the Prince
Islands and central Marmara Sea (severe but 10 times less
powerful than a Mw 8.0)
 Possibly more than one event in a year (sequence, aftershocks)
11(Armijo, Pondard, Meyer and the MARMARASCARPS Cruise Party,
2005)
(Pondard et al., 2007)

12. Understanding Natural Hazards
 Secondary perils: e.g. Liquefaction, Landslides, Tsunami,
Fire following
 Use of observed loss information and/or hazard data to
quantify non-modelled risk
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Liquefaction potential
(KOERI, 2002)
ChiChi earthquake, Taiwan, 1999

13. Exposure data – the core of any
view of risk
 Complete understanding of data and assumptions
13

14. Exposure analysis
 Putting your exposure in context
 Identify and review key exposure accumulations
14

15. Exposure Analysis
 Evaluation of how much exposure is at risk
 Geographic distribution
 Inventory
 Year on year volatility
15

16. Exposure Analysis
 Geographic resolution
Photo courtesy of Aykut Barka(1999)
50 km
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17. Exposure Benchmarking
 Exposure comparisons to alternative data sources (e.g., World
Bank distribution of building stock)
 Developing a comprehensive understanding of the portfolio
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18. Exposure Benchmarking
 Peer comparison of data capture. Larger polygon = better data
 Reinsurers tend to ‘load’ prices for uncertainty, where data is not
known or poorly captured
 Where data quality is good, leverage to get more affordable cover
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19. Validation - Sensitivity analysis
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 How changes in exposure inputs impact loss outputs
 Post-2000 buildings, significant improvements in design codes in
Turkey.
 33% drop in losses from masonry to reinforced concrete with MRF
Turkey Cresta Construction RMS v11.0
0.00%
10.00%
20.00%
30.00%
0 200 1,000400 600 800
Return Period (Years)
LossRatio
Masonry
Reinforced
concrete
Unknown
Reinforced
concrete MRF
w/ URM
Wood frame
(modern)
Steel
33%

20. Portfolio management
 Impact of portfolio mix on PML and reinsurance costs
 Leveraging cat model outputs to optimise the portfolio cat
exposure
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21. Portfolio management
 Allocation of portfolio level catastrophe risk to underlying
business units, split by peril
 Transparent allocation method. Can be split by peril and
layer
 Ensures your view of risk consistently feeds through into
underwriting, pricing, and capital management
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22. Regulation
 Support through the entire internal model approval process for
Cat risk
 Justifying the view of Cat risk you use is robust and credible
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23. Conclusions
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 Crucial: Internal strategy to collect detailed exposure data
 Validation: In depth understanding of hazards, model
assumptions and limitations
 And only then: Use it to support risk management decisions,
based on your own view of risk

24. Subtitle 1
Subtitle 2
Subtitle 3
THANK YOU!

25. Major historical events - Marmara
25(Pondard et al.,2007)