Introduction

1. Introduction • Two types of data for catastrophe analysis • Exposures treated as inputs • Market Share • Location Specific • Losses are simulated from the models • Experience is sparse • Models are black boxes • Common Problems

2. Exposures • Allows for management of exposures • By geographic region • By LOB • By Peril

3. Market Share Models • Advantages • Limited amount of data needed • Premium • State • County • Sum of Insured by county • Limits • Value • Ease of use

4. Market Share Models • Disadvantages • Limited modeling of reinsurance • Excess • Per Risk • Facultative • Deductible not taken into account

5. Location Specific Catastrophe Runs • Advantages • Specific policy information • Reinsurance information • Disadvantages • Time consuming • More data required

6. Data Requirements for Location Specific Catastrophe Runs • Physical Characteristics of risk • Primary • Construction type • Occupancy • Secondary • Height • Age • Etc.

7. Data Requirements for Location Specific Catastrophe Runs • Financial Characteristics • Location Information • Value • Limit • Deductible • Site Information • Limit • Deductible

8. Data Requirements for Location Specific Catastrophe Runs • Policy Information • Blanket limit • Blanket deductible

9. Data Requirements for Location Specific Catastrophe Runs • Reinsurance Structure • Excess Information • Attachment points • Limits • Treaty Information • Inuring order • Terms

10. Data Requirements for Location Specific Catastrophe Runs • Definitions of a risk • One building, one location • Multiple buildings at one location

11. Loss Data • The output from a catastrophe model is simulated loss data. • Annual frequency • Annual severity • Simulating reinsurance structures by integrating a insurers property book with the catastrophe losses.

12. Uses of Losses • Losses by event, by peril • Exceeding probability curves • Expected losses by layer • Manageable file sizes • Losses by event, by region, by peril • Expected loss by region • Probability of losses exceeding a fix amount in a region • Larger files sizes

13. Uses of Losses • Losses by event, by region, by policy, by peril • Marginal PMLs • Output files can be large.

14. Common Problems • Only billing address available • No information on number or amount of liability of structures being insured • Only policy information not the underlying structure information. Damage curves based on value not limit.

15. Common Problems • No deductible information • There is an intrinsic tradeoff between how much specific detail to capture for the modeling and the gains from the extra information.

16. Common Problems • There is the problem with geocoding companies’ exposures. The is always differences between the Postal Code database used by the capturing company and the database used by the model.