World Bank Conference on Financing Disaster Risk, Washington, 2003

Catastrophe Risk Models for Asia

from the User Perspective

George WalkerHead of Strategic Developments

Aon Re Australia

Financing the Risks of Natural Disasters

World Bank Conference on Financing Disaster Risk, Washington, 2003

Hypothetical Case Study

OJUDAKAN

Population 10 Million

GDP/Person 15% US

GDP growth 4 % / year

Significant Earthquake & Typhoon Risk

Dwellings 2 Million

Faults

Typhoon Tracks

World Bank Conference on Financing Disaster Risk, Washington, 2003

Ojudakan Government under pressure from international funding agencies to

• Reduce vulnerability of housing

• Introduce a national disaster insurance scheme

Catastrophe Insurance Situation

Insurance Vulnerability

Large Industrial (Multi-National) 100 % Low

Smaller Industrial/Commercial 40 % Moderate

Public Infrastructure 0 % Low

Housing 5 % High

World Bank Conference on Financing Disaster Risk, Washington, 2003

Design of Disaster Insurance Schemes

Financial Arrangements

Premium Collection & Claims Management

Administrative Structure

Disaster Insurance Scheme

Premiums

Policy Conditions

Affordability

Sustainability

Operations

Hazard Risks

Building Vulnerabiity

Building Inventory

World Bank Conference on Financing Disaster Risk, Washington, 2003

Key Output From Loss Risk Analysis

0

1000

2000

3000

0 200 400 600Event Loss Return Period (Years)

Eve

nt

Lo

ss (

US

$ M

illi

on

)

Exceedance Loss Risk Curve & Table

Year 1

Year 10Year 20

World Bank Conference on Financing Disaster Risk, Washington, 2003

Average Annual Loss = dL

T

From Loss Curve

Can also evaluate associated standard deviation

Insured

Loss (L)

Return Period (T)

PML

Market Value Premium =

Function (Average Annual Loss, Standard Deviation)+ Local Factors

Premium Analysis

World Bank Conference on Financing Disaster Risk, Washington, 2003

Corporate

Funds

Pre

miu

ms

Cla

ims

Investments

Management

GovernmentRefunds

Taxes

Costs

CUSTOMERS

Gains

Losses

BorrowingsCapital

Interest

Risk Financing

Premiums

Claims

Sustainability Modelling

Model statistically performance over time

World Bank Conference on Financing Disaster Risk, Washington, 2003

Sustainability Analysis – Output

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

0 10 20 30 40 50Years

Med

ian

Fu

nd

/PM

L

No ReinsuranceProb of Ruin 7.2%

Full ReinsuranceProb of Ruin 3.6%

Initial Fund Size = Zero

Annual Growth Rate – PML & Premium) 4%Investment return rate 6%Loan rate 7%Admin Costs 5%Initial Premium US$10/dwelling

World Bank Conference on Financing Disaster Risk, Washington, 2003

Earthquake Loss Model

Insured ValueAgeBuilding TypeBuilding usePolicy conditions

Intensity

Loss

Ratio

0

1

BrittleDuctile

World Bank Conference on Financing Disaster Risk, Washington, 2003

GIS Typhoon Loss Model

Insured ValueAgeBuilding TypeBuilding usePolicy conditions

Wind Speed

Loss

Ratio

0

1

Code Non-Code

Flood Depth

Loss

Ratio

0

1

1 storey

Multi-Storey

Wind Speed Contours

Flood Depths

World Bank Conference on Financing Disaster Risk, Washington, 2003

Modelling Problem - Hazard Risk

Lack of Reliable Scientific Data

Faults Poor

Earthquake Records (M>5) Moderate

Typhoon Records Moderate

Soil Mapping Poor

Flood Mapping Poor

Topographical Mapping Poor

Data Probable Information

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 non- standard 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

Example

Return Period (Years)

Lo

ss (

$ M

illio

n)

Return Period (Years)

Lo

ss (

$ M

illio

n)

Tropical Cyclone (Wind) Earthquake (Wind)

Model A

Model B Model A

Model B

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