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Financing the Risks of Natural Disasters
Catastrophe Risk Models for Asia
from the User Perspective
George Walker
Head of Strategic Developments
Aon Re Australia
World Bank Conference on Financing Disaster Risk, Washington, 2003
Hypothetical Case Study
OJUDAKAN
Population 10 Million
Dwellings 2 Million
GDP/Person
15% US
GDP growth 4 % / year
Significant Earthquake
& Typhoon Risk
Faults
Typhoon Tracks
World Bank Conference on Financing Disaster Risk, Washington, 2003
Catastrophe Insurance Situation
Insurance Vulnerability
Large Industrial (Multi-National)
100 %
Low
40 %
Moderate
Public Infrastructure
0%
Low
Housing
5%
High
Smaller Industrial/Commercial
Ojudakan Government under pressure from
international funding agencies to
• Reduce vulnerability of housing
• Introduce a national disaster insurance scheme
World Bank Conference on Financing Disaster Risk, Washington, 2003
Design of Disaster Insurance Schemes
Affordability
Hazard Risks
Building Vulnerabiity
Premiums
Building Inventory
Policy Conditions
Sustainability
Financial Arrangements
Premium Collection & Claims Management
Operations
Administrative Structure
Disaster Insurance Scheme
World Bank Conference on Financing Disaster Risk, Washington, 2003
Key Output From Loss Risk Analysis
Event Loss (US$ Million)
Exceedance Loss Risk Curve & Table
3000
Year 20
Year 10
2000
Year 1
1000
0
0
200
400
Event Loss Return Period (Years)
World Bank Conference on Financing Disaster Risk, Washington, 2003
600
Premium Analysis
From Loss Curve
PML
Insured
Average Annual Loss =
Loss (L)

Can also evaluate associated
standard deviation
Return Period (T)
Market Value Premium
=
Function (Average Annual Loss, Standard Deviation)
+ Local Factors
World Bank Conference on Financing Disaster Risk, Washington, 2003
dL
T
Sustainability Modelling
Premiums
Claims
C
U
S
T
O
M
E
R
S
Premiums
Risk Financing
Corporate
Claims
Funds
Borrowings
Investments
Management
Government
Model statistically performance over time
World Bank Conference on Financing Disaster Risk, Washington, 2003
Sustainability Analysis – Output
Initial Fund Size = Zero
1.75
Annual Growth Rate – PML & Premium)
Investment return rate
Loan rate
Admin Costs
Initial Premium US$10/dwelling
Median Fund/PML
1.50
1.25
4%
6%
7%
5%
1.00
0.75
No Reinsurance
Prob of Ruin 7.2%
0.50
Full Reinsurance
Prob of Ruin 3.6%
0.25
0.00
0
10
20
30
Years
World Bank Conference on Financing Disaster Risk, Washington, 2003
40
50
Earthquake Loss Model
Insured Value
Age
Building Type
Building use
Policy conditions
1
Loss
Brittle
Ratio
0
Intensity
World Bank Conference on Financing Disaster Risk, Washington, 2003
Ductile
GIS Typhoon Loss Model
Insured Value
Age
Building Type
Building use
Policy conditions
Flood Depths
1
Loss
Code
NonCode
Ratio
0
Wind Speed
1
Wind Speed Contours
Loss
Ratio
1
storey
MultiStorey
0
Flood Depth
World Bank Conference on Financing Disaster Risk, Washington, 2003
Modelling Problem - Hazard Risk
Lack of Reliable Scientific Data
Data
Probable Information
Faults
Poor
Earthquake Records (M>5)
Moderate
Typhoon Records
Moderate
Soil Mapping
Poor
Flood Mapping
Poor
Topographical Mapping
Poor
World Bank Conference on Financing Disaster Risk, Washington, 2003
Modelling Problem - Portfolio Data
Information often lacking of national inventory of
buildings.
Where information exists likely to be deficient in
respect of
• Value
• Precise Location – often aggregated at
coarse level
• Building characteristics relevant to
vulnerability - eg age, construction type,
roof type, number of stories, occupancy type
World Bank Conference on Financing Disaster Risk, Washington, 2003
Modelling Problem - Vulnerability
Information generally lacking on vulnerability of
local forms of construction
Further complicated by need to to
•
Allow for effect of mitigation measures such
as building code changes in modelling future
losses
•
Be able to model losses when using nonstandard policy conditions – eg ‘total loss’
claims only.
World Bank Conference on Financing Disaster Risk, Washington, 2003
Consequences
Heavy Reliance on Expert Opinion
And
Extrapolation of 1st World Models
Result
•
Models may not be relevant – eg Typhoon loss
models based on wind damage when flooding
main hazard
•
Different models may give widely differing
answers
World Bank Conference on Financing Disaster Risk, Washington, 2003
Model A
Model B
Return Period (Years)
Tropical Cyclone (Wind)
Loss ($ Million)
Loss ( $ Million)
Example
Model B
Model A
Return Period (Years)
Earthquake (Wind)
Differences obtained in using Australian commercial
loss models
Note: These are worst case examples – depends on portfolios and
sophistication of data
World Bank Conference on Financing Disaster Risk, Washington, 2003
Underlying Issue
Cost of Developing & Maintaining Models
Need large amount of local knowledge
Expensive if all done in 1st World
Not commercially viable for many countries
Suggested Solution
Fund local researchers to develop national
consensus standard models for vulnerability
and hazard risk which would be freely
available to all catastrophe loss modellers
World Bank Conference on Financing Disaster Risk, Washington, 2003
World Bank Conference on Financing Disaster Risk, Washington, 2003