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

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Presentation on theme: "Adding a Cat Load to Property Reinsurance Pricing One Reinsurer’s Approach June 1, 2005 - CAGNY."— Presentation transcript:

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

2 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 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 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 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 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/ /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 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 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 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 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 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 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 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 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 15 What your results look like if you model the Cat and Non-Cat separately  Using the assumptions on the previous page:

16 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 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 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.

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