1. World Bank Conference on Financing Disaster Risk, Washington, 2003 Catastrophe Risk Models for Asia from the User Perspective George Walker Head of Strategic Developments Aon Re Australia Financing the Risks of Natural Disasters

2. 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

3. 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/Commercial40 % Moderate Public Infrastructure 0 %Low Housing 5 % High

4. 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

5. World Bank Conference on Financing Disaster Risk, Washington, 2003 Key Output From Loss Risk Analysis 0 1000 2000 3000 0200400600 Event Loss Return Period (Years) Event Loss (US$ Million) Exceedance Loss Risk Curve & Table Year 1 Year 10 Year 20

6. 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

7. World Bank Conference on Financing Disaster Risk, Washington, 2003 Corporate Funds Premiums Claims Investments Management Government Refunds Taxes Costs CUSTOMERSCUSTOMERS Gains Losses Borrowings Capital Interest Risk Financing Premiums Claims Sustainability Modelling Model statistically performance over time

8. 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 01020304050 Years Median Fund/PML No Reinsurance Prob of Ruin 7.2% Full Reinsurance Prob of Ruin 3.6% Initial Fund Size = Zero Annual Growth Rate – PML & Premium) 4% Investment return rate6% Loan rate7% Admin Costs5% Initial Premium US$10/dwelling

9. World Bank Conference on Financing Disaster Risk, Washington, 2003 Earthquake Loss Model Insured Value Age Building Type Building use Policy conditions Intensity Loss Ratio 0 1 Brittle Ductile

10. World Bank Conference on Financing Disaster Risk, Washington, 2003 GIS Typhoon Loss Model Insured Value Age Building Type Building use Policy 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

11. 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

12. 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

13. 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.

14. World Bank Conference on Financing Disaster Risk, Washington, 2003 Consequences Heavy Reliance on Expert Opinion And Extrapolation of 1 st 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

15. World Bank Conference on Financing Disaster Risk, Washington, 2003 Example Return Period (Years) Loss ( $ Million) Return Period (Years ) Loss ($ Million ) 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

16. 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 1 st 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

17. World Bank Conference on Financing Disaster Risk, Washington, 2003