Adjustments to Cat Modeling

CAS Seminar on RenisuranceSean DevlinMay 7-8, 2007

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Model Selection

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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

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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

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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

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Model Selection

Weights

Zone CT RMS EQECA EQ 70% 0% 30%Japan EQ 50% 0% 50%FL WS 0% 100% 0%Euro Wind 20% 40% 40%

Factors

Zone CT RMS EQECA EQ 80% 150% 130%Japan EQ 80% 120% 125%FL WS 90% 120% 50%Euro Wind 150% 80% 110%

Use One Model By “Territory” – An Example

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Model Selection

Use Multiple ModelsBenefits

Can see differences by deal and in generalDrawbacks

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

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Climate and

Hurricane Prediction

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TCNA Adjustments - Climate

Intensity AEF NOAA TSR CSU Actual Average

Named Storms 13.2 12-15 13.9 15 27 9.9

All Hurricanes 7.5 7-9 7.8 8 15 6.0

Major Hurricanes

3.7 3-5 3.6 4 7 2.6

Intensity AEF NOAA TSR CSU Actual Average

Named Storms 14.5 13-16 15.4 17 10 9.9

All Hurricanes 8.5 8-10 8.2 9 5 6.0

Major Hurricanes

3.7 4-6 3.8 5 2 2.6

Despite impressive science, the individual season predictions, the last two years was off the mark.

2005

2006

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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

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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

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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

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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

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Model Adjustments

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TCNA Adjustments – Frequency/Severity

Adjust whole curve equally

Ignores shape change

Treats all regions equally

Adjust whole curve by return period/region

0%

50%

100%

150%

200%

250%

ST/LT 1

ST/LT 2

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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

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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

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“Unmodeled” Exposure

Tornado/Hail

Winter Storm

Wildfire

Flood

Terrorism

Fire Following

Other

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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

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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?????

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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

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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

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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

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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

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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