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Adjustments to Cat Modeling

Adjustments to Cat Modeling. CAS Seminar on Renisurance Sean Devlin May 7-8, 2007. Model Selection. Model Selection. Major modeling firms AIR EQE RMS Other models, including proprietary Options in using the models Use one model exclusively Use one model by “territory”

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Adjustments to Cat Modeling

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  1. Adjustments to Cat Modeling CAS Seminar on Renisurance Sean Devlin May 7-8, 2007

  2. Model Selection

  3. Model Selection • Major modeling firms • AIR • EQE • RMS • Other models, including proprietary • Options in using the models • Use one model exclusively • Use one model by “territory” • Use multiple models for each account

  4. Model Selection • Use One Model Exclusively • Benefits • Simplify process for each deal • Consistency of rating • Lower cost of license • Accumulation easier • Running one model for each deal involves less time • Drawbacks • Can’t see differences by deal and in general • Conversion of data to your model format

  5. Model Selection • Use One Model By “Territory” • Detailed review of each model by “territory” • Territory examples (EU wind, CA EQ, FL wind) • Select adjustment factors for the chosen model • Benefits • Simplify process for each deal • Consistency of rating • Accumulation easier • Running one model involves less time • Drawbacks • Can’t see differences by deal • Conversion of data to your model format

  6. Model Selection Use One Model By “Territory” – An Example

  7. Model Selection • Use Multiple Models • Benefits • Can see differences by deal and in general • Drawbacks • Consistency of rating? • Conversion of data to each model format • Simplify process for each deal • High cost of licenses • Accumulation difficult • Running one model for each deal is time consuming

  8. Climate and Hurricane Prediction

  9. TCNA Adjustments - Climate Despite impressive science, the individual season predictions, the last two years was off the mark. 2005 2006

  10. TCNA Adjustments - Climate • Option 1 - Find no credibility in the forecasts • Use 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

  11. 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

  12. 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

  13. 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

  14. Model Adjustments

  15. TCNA Adjustments – Frequency/Severity • Adjust whole curve equally • Ignores shape change • Treats all regions equally • Adjust whole curve by return period/region

  16. Modeled Perils – Other Adjustments • Actual vs. Modeled – look for biases (Macro/Micro) • Model recent events with actual portfolio • More confidence on gross results, but some insight may be gained on per risk basis • One or two events may show a material upward miss. Key is to understand why. • Exposure Changes / Missing Exposure/ITV Issues • TIV checks/audits • Scope of data – international, all states & perils • Changes in exposure, important for specialty writers

  17. Modeled Perils – Other Adjustments • Other Biases in modeling • LAE • Fair plans/pools/assessments – know what is covered by client and treaty prospectively • FHCF – Reflect all probable outcomes of recovery • Storm Surge • Demand Surge • Pre Event • Post Event

  18. “Unmodeled” Exposure • Tornado/Hail • Winter Storm • Wildfire • Flood • Terrorism • Fire Following • Other

  19. 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 • 2001: 2.2B • 2002: 1.7B • Methodology • Experience and exposure ate • Compare to peer companies with more data • Compare experience data to ISO wind history • Weight methods

  20. Unmodeled Perils Winter storm • Not insignificant peril in some areas, esp. low layers • 1994: 100M, 175M, 800M, 105M • 1993: 1.75B • 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 • Aggregate Cover?????

  21. Unmodeled Perils Wildfire • Not just CA • Oakland Fires: 1.7B • 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

  22. 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 2007/8 • Other – depends on data

  23. 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 • Reflect difference in policy T&Cs

  24. 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

  25. 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

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