Adjustments to Cat Modeling

CAS Seminar on CatSean DevlinSeptember 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

0%

50%

100%

150%

200%

250%

ST/LT 1

ST/LT 2

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 – 12th largest 2001: 2.2B – 15th largest 2002: 1.7B – 21st 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 – 18th 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 – 19th 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