Adjustments to Cat Modeling CAS Seminar on Cat Sean Devlin September 18, 2006

Slide 2 TCNA Adjustments - Climate Options on Using Climate Forecasts  Find no credibility in the forecasts  Believe that the forecasts are directionally correct  Believe completely in the multi-year forecasts  Believe completely in the single year forecasts

Slide 3 TCNA Adjustments - Climate Option 1 - Find no credibility in the forecasts  Use the a vendor model based on long term climate  Adjust the loss curve down of a vendor model that has increased frequency/severity  Use own model  A blend of the above

Slide 4 TCNA Adjustments - Climate Option 2- Believe that the forecasts are directionally correct  Credibility weighting between models in option 1 and a model with frequency adjustments  Adjust a long-term model for frequency/severity  Adjust long-term version of a vendor model  Adjust own model for frequency/severity  Combination of the above

Slide 5 TCNA Adjustments - Climate Option 3 - Believe completely in the multi-year forecasts  Implement a vendor model with a multi-year view  Make frequency/severity adjustments to a long term vendor model  Adjust own model  Blend of the above

Slide 6 TCNA Adjustments - Climate Option 4 - Believe completely in the single year forecasts  Implement seasonal forecast version for a vendor model  Adjust vendor model for frequency/severity  Adjust internal model for frequency/severity  Combination of the above

Slide 7 TCNA Adjustments – Frequency/Severity Adjust whole curve equally  Ignores shape change  Treats all regions equally Adjust whole curve by return period/region

Slide 8 Modeled Perils – Other Adjustments Actual vs. Modeled – look for biases (Macro/Micro) Other Biases in modeling Exposure Changes / Missing Exposure/ITV Issues LAE Fair plans/pools/assessments Demand Surge  Pre Event  Post Event

Slide 9 Unmodeled Exposure Tornado/Hail Winter Storm Wildfire Flood Terrorism Fire Following Other

Slide 10 Unmodeled Perils Tornado Hail  National writers tend not to include TO exposures  Models are improving, but not quite there yet  Significant exposure  Frequency: TX  Severity: 2003: 3.2B – 12 th largest 2001: 2.2B – 15 th largest 2002: 1.7B – 21 st largest  Methodology  Experience and exposure Rate  Compare to peer companies with more data  Compare experience data to ISO wind history  Weight methods

Slide 11 Unmodeled Perils Winter storm  Not insignificant peril in some areas, esp. low layers  1994: 100M, 175M, 800M, 105M  1993: 1.75B – 18 th largest  1996: 600M, 110M, 90M, 395M  2003: 1.6B  # of occurrences in a cluster?????  Possible Understatement of PCS data  Methodology  Degree considered in models  Evaluate past event return period(s)  Adjust loss for today’s exposure  Fit curve to events

Slide 12 Unmodeled Perils Wildfire  Not just CA  Oakland Fires: 1.7B – 19 th largest  Development of land should increase freq/severity  Two main loss drivers  Brush clearance – mandated by code  Roof type (wood shake vs. tiled)  Methodology  Degree considered in models  Evaluate past event return period(s), if possible  Incorporate Risk management, esp. changes  No loss history - not necessarily no exposure

Slide 13 Unmodeled Perils Flood  Less frequent  Development of land should increase frequency  Methodology  Degree considered in models  Evaluate past event return period(s),if possible  No loss history – not necessarily no exposure Terrorism  Modeled by vendor model? Scope?  Adjustments needed  Take-up rate – current/future  Future of TRIA – exposure in 2006  Other – depends on data

Slide 14 Unmodeled Perils Fire Following  No EQ coverage = No loss potential? NO!!!!!  Model reflective of FF exposure on EQ policies?  Severity adjustment of event needed, if  Some policies are EQ, some are FF only  Only EQ was modeled  Methodology  Degree considered in models  Compare to peer companies for FF only  Default Loadings for unmodeled FF  Multiplicative Loadings on EQ runs

Slide 15 Unmodeled Perils Other Perils  Expected the unexpected  Examples: Blackout caused unexpected losses  Methodology  Blanket load  Exclusions, Named Perils in contract  Develop default loads/methodology for an complete list of perils

Slide 16 Summary Don’t trust the Black Box  Understand the weakness/strengths of model  Know which perils/losses were modeled  Perform reasonability checks  Add in loads to include ALL perils  Reflect the prospective exposure