Adding a Cat Load to Property Reinsurance Pricing

1. Adding a Cat Load to Property Reinsurance Pricing One Reinsurer’s Approach June 1, 2005 - CAGNY

2. Agenda • Early Disclaimers • Property Reinsurance Pricing: Laying the Groundwork before adding a Cat Load • What do you do with Cat Modeling Input and Output? • How do you incorporate a Cat Load into Cash Flow Modeling? • Can you judge a company by its Cat Modeling? • Questions/Comments

3. Early Disclaimers • Scope of discussion • Not HOW to run cat models • Rather, analyzing inputs and outputs • Focus on RMS • Types of treaties • Per Risk • Quota Shares • Endurance in N.A. doesn’t price pure cat treaties • More ways to “skin the cat” than presented here • Comments and suggestions welcome!

4. Property Reinsurance Pricing: Getting the ball rolling… • Analyze cat vs. non-cat separately • Exposure rate • PSOLD, Loss to Value Curves, etc. • Use gross non-cat loss ratios • Experience rate • Both non-cat and cat only basis • Consider including some cats in non-cat analysis • Hurricanes w/significant flood (Floyd, Allison) • Tornado and hail events • Once non-cat burn is selected, add cat load • Monte Carlo Simulation models are used to value any loss sensitive features.

5. Examining your EDM: Avoiding “Garbage in, Garbage out” • EDM Content • Perils • Regions • Examine “Post Import Summary” • % of locations with • street address • construction code • occupancy code • Compare to prior years’ Summary • Compare TIVs with limits profile • How old is the EDM?

6. Trending the EDM prior to modeling • “Average exposure date”: 6 months prior to EDM date stamp • Example: Date Stamp = 12/31/2004 • EDM has policies in force at 12/31/2004 • These policies incept 1/1/2004 - 12/31/2004 • 7/1/2004 is average exposure date • Trend TIVs to prospective treaty period • Average prospective date of loss = ‘trend to’ date • Damage curve based on property values at time of loss

7. Dealing with your Output: What do you do with your results? • Treaty cat loss ratio • (Modeled treaty cat loss) / (Inforce on-leveled premium) • Onlevel consistent with EDM date stamp • Note: not PROSPECTIVE Subject Premium! • Ratio would be too low if real growth in portfolio. • Example: 2004 EDM produces losses of 2M • 2004 WP = 20M • 2005 WP = 35M due to expansive growth • Cat loss ratio = 2M / 20M • On-level for rate changes. • Otherwise, ratio too low if there were rate decreases • Example: 2004 EDM produces losses of 3M • 2004 WP = 30M • Onlevel 2004 premium at 2005 rates = 25M • Cat loss ratio = 3M / 25M • Adjust for any part of Subject Premium not covered by cat model (e.g. International)

8. What happens if you only get aggregate cat modeling data for a per risk treaty? • Suppose client unable to provide EDM • If Unicede file (aggregate data) available, run Catrader to get gross losses • Use gross cat loss ratio in exposure rating model • Allow property curves to layer gross cat losses • We reselect curves that give more weight to wind • There may be other methods to consider, but since we are more of an RMS company, this is what we do.

9. Examining your Cat Experience • Take a longer time horizon • Example: may choose 5 year average for non-cat, but all year average for cat • Has the book shifted? • More coastal exposure? • Change in management? • Other?

10. How do you choose between Cat Experience and Cat Modeling Results? • Shifts in the book • Has management changed the book’s direction? • Limits shifting up or down • More or less cat exposed • Changes in terms and conditions • Loss data quality • EDM data quality • Validity of Cat Model for these exposures & policies • Agreement of modeled results with recent experience • How much weight would you EVER give to cat experience anyway?

11. Loss Sensitive Features: Why including a Cat Distribution matters • If you model all your property exposure using just one distribution, you are likely missing the inherent volatility in the cat; you are subsequently understating the value that the loss sensitive feature could have. This could lead you to make a decision that you may one day regret. • And that day usually happens between August and November, in places like Florida.

12. Example: • Assumptions: • Subject Premium = 50M • Total Loss Ratio = 60% • Non-cat Loss Ratio = 30% • Cat Loss Ratio = 30% • Ceding Commission = 27.5% • Brokerage = 1% • Profit Commission = 30% after 20% • One year deal; no deficit/credit carryforwards considered

13. What your results look like if you use a lognormal to model all losses together • Assume a mean of 60% with a CV of 15%

14. Modeling the Cat and Non-Cat separately - Assumptions • Assume a non-cat mean of 30% with a CV of 10%, a cat mean of 30% and a cat distribution from RMS’s AEP curve.

15. What your results look like if you model the Cat and Non-Cat separately • Using the assumptions on the previous page:

16. Can you judge a company by its Cat Modeling? • Meeting the company’s cat modeler can clarify • Company’s pricing of property business • How company assesses cat risk • How much company values data quality • How well company can monitor and control its book • Understanding what the client deems important can give you great insight over whether they are someone you even want to reinsure. • Any reinsurer has finite cat capacity: so must rank clients to reflect differing levels of quality in making underwriting decisions.

17. The Spanish Inquisition: Cat Style • Do you run Riskbrowser “pre-binding” or “post-binding”? • Do you run all regions for all perils? • How diligent are you about capturing street address? Construction code? Occupancy code? • Do you “turn on” demand surge? Storm surge? • What about secondary uncertainty? • How do you think about capital allocation? • How often do you “roll up” your portfolio? • How often do you inspect insured locations? • Do you use an external source to help keep up with proper valuations? • Do you really know the values of those 25,000 locations on that large schedule of properties?

18. Some definitions • Primary uncertainty • Whether or not an event will occur, and if an event does occur, which event it will be. • Secondary uncertainty • Uncertainty in the size of loss, given that a specific event has occurred. • Demand Surge • Increases in claims costs following a major event, due to economic, social, and operational factors in the post-event environment. • Storm surge • Rising ocean water levels along hurricane coastlines that can cause widespread flooding.