Catastrophe
Pricing:
The Finer Points
Sean Devlin
CARe Meeting
June 6-7, 2005
Agenda
 Vendor
Modeling Process
 Evaluating Inputs
 Unmodeled Perils
 Evaluating Outputs
 Conversion of Loss Cost to Pricing
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GE Insurance Solutions
June 6-7, 2005
Vendor Models –What to Use?
 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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GE Insurance Solutions
June 6-7, 2005
Vendor Models –What to Use?
(Cont’d)
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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GE Insurance Solutions
June 6-7, 2005
Vendor Models –What to Use?
(Cont’d)
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
5
GE Insurance Solutions
June 6-7, 2005
Vendor Models –What to Use?
(Cont’d)
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
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GE Insurance Solutions
June 6-7, 2005
Model Inputs
 Garbage In => Garbage Out
 TIV checks/ aggregates
 “As-if” past events
 Scope of data (e.g. RMS – WS, EQ, TO datasets)
 Which “territory” modeled and not modeled
 Type of country considered for exposures abroad
 Clash between separate zones (US – Caribbean)
 Tier I – well established models – US, EU, etc.
 Tier II – modeled, but less reliable – SA, Caribbean
 Tier III – not modeled
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GE Insurance Solutions
June 6-7, 2005
“Unmodeled” Perils
Winter storm
 Not insignificant peril in some areas, esp. low layers
 1993: 1.75B – 14th largest
 1994: 100M, 175M, 800M, 105M
 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
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GE Insurance Solutions
June 6-7, 2005
“Unmodeled” Perils (cont’d)
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
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GE Insurance Solutions
June 6-7, 2005
“Unmodeled” Perils (cont’d)
Wildfire
 Not just CA
 Oakland Fires: 1.7B – 15th 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
 Risk management, esp. changes
 No loss history – not necessarily no exposure
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GE Insurance Solutions
June 6-7, 2005
“Unmodeled” Perils (cont’d)
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
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GE Insurance Solutions
June 6-7, 2005
“Unmodeled” Perils (cont’d)
Extratropical wind
 National writers tend not to include TO exposures
 Models are improving, but not quite there yet
 Significant exposure
 Frequency: TX
 Severity: May 2003 event of 10B – 9th largest
 Methodology
 Experience and exposure Rate
 Compare to peer companies with more data
 Compare experience data to ISO wind history
 Weight methods
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GE Insurance Solutions
June 6-7, 2005
“Unmodeled” Perils (cont’d)
No Data
 Typically for per risk contracts without detailed data
 Typically not a loss driver on per risk treaties
 However, exceptions exist
 Methodology
 Experience and exposure Rate
 Compare to peer companies with modeling
 Develop default loads by layer/location
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GE Insurance Solutions
June 6-7, 2005
“Unmodeled” Perils (cont’d)
Other Perils
 Expected the unexpected – Dave Spiegler article
 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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GE Insurance Solutions
June 6-7, 2005
Using the Output
Don’t Trust the Black Box
 Data, Data, Data
 Contract Match:
 Definition of risk
 Definition occurrence
 Dual trigger contracts
 Scope of coverage
 Modeling of past exposures
 Need to convert to prospective period
 TIV inflation
 Change in exposures
 Know what assumptions were used by modeler
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GE Insurance Solutions
June 6-7, 2005
Loadings to final EL
Considerations in final indicated “price”
 % of loss?
 % of s?
 Combination of above?
 Target LR, TR, CR?
 Reflect red zone capacity constraints?
 “Unused” capacity loads
 EL for Layer 100M x 100M is 5M
 EL for Layer 200M x 100M is 5.1M
 Loading for 100M x 200M??????
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GE Insurance Solutions
June 6-7, 2005
Summary
 Determine process and models to use
 Know what was modeled
 Perform reasonability checks
 Understand strength and weakness of the models
 Add in the “unmodeled” exposure
 Make other adjustments to reflect ongoing terms and
exposure
Don’t Trust the Black Box
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GE Insurance Solutions
June 6-7, 2005