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Property Reinsurance Ratemaking. Sean Devlin Reinsurance Boot Camp on Pricing Techniques July 29, 2005. Agenda. Background ELR determination Primary “Price” Experience Rating Exposure Rating Weighting of Methods Catastrophe Loads and Issues Conversion of Loss Cost to Pricing

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property reinsurance ratemaking

Property Reinsurance Ratemaking

Sean Devlin

Reinsurance Boot Camp

on Pricing Techniques

July 29, 2005

agenda
Agenda
  • Background
  • ELR determination
  • Primary “Price”
  • Experience Rating
  • Exposure Rating
  • Weighting of Methods
  • Catastrophe Loads and Issues
  • Conversion of Loss Cost to Pricing
  • Summary and Questions
background
Background
  • My past experience, particularly
    • AmRe: 3 years of leading Finite, National and Specialty business pricing
    • GE: 3 years leading Global Property Product Pricing
  • What I have seen
    • Common mistakes,
    • Emerging exposures
    • Worst and best of the market
    • The most complex treaties
    • Management of a global portfolio and its effect on strategy and pricing
elr determination
ELR Determination

Foundation of Exposure Rating

  • Which ELR to use?
    • Must match your curve in exposure rating
    • Preference: Eliminate cat as much as possible
    • Options for ELR:
      • Full LR
      • No cat whatsoever
      • Exclude certain cats
  • Methodology
    • Equivalent to primary ratemaking, except
    • Need for factors to back out certain cats to match exposure curve, if the match isn’t already made
elr calculation per risk pro rata
ELR Calculation - Per Risk/Pro Rata

Determining your ELR

  • Breakout components
    • Basic LR – very stable small, non-cat events
    • Risk LR – losses subject to a per risk Layer
      • Breakout into layers, like per risk rating
      • Appropriate blend of experience & exposure
    • Small Cat LR(s) – experience rate vs. model
    • Modeled Cats
  • More reasons for breakout?
    • Inuring reinsurance or contract features
    • Understand the drivers of the ELR
    • Appropriate targets for quoting business
elr determination1
ELR Determination

Trend Parameters

  • Cost of contracting labor
  • Size of homes increasing
  • Deductible impacts on frequency and severity
  • Data – shifts in and out of E&S market
  • Excess business
  • Non-standard classes
  • Demand surge
note on primary price
Note on Primary “Price”

Price Monitoring Reports

  • Typically created to measure price lift circa 2000
  • Know what is (isn’t) captured
    • Filed rate changes
    • Schedule modification factors
    • Experience modification factors
    • SIR/Limit
    • Terms and conditions
    • New business
  • Test for bias
    • Trend or shift in adjusted loss ratios
    • Discuss with client changes
    • More important for high capacity eaters
note on primary price1
Note on Primary “Price”

Effect of missing uncaptured price

  • Typically underestimated the magnitude of change
  • Softening Cycle:
    • Underestimating decreased rates
    • Underestimating reserves
    • Calendar year results lag true results
    • Delays recognition of results
    • Softening prolonged– damage is slowly realized
  • Hardening Cycle:
    • Underestimating increased rates
    • Overestimating reserves
    • Calendar year results lag true results
    • Delays recognition of results
    • Hardening prolonged– success is slowly realized
primary price cont d1
Primary “Price” (cont’d)

“Uncaptured” Rate change

primary price cont d2
Primary “Price” (cont’d)

Actual peak of soft market

Calendar Year results understated during soft market

Should be hardening here

experience rating
Experience Rating

Premium Side

  • Same as pro rata, mostly
  • Splitting up business into exposed and not exposed
  • In split business, parameters may be different
  • Exiting class? Reflect all premium affected if excl.

Loss Side

  • Capping at policy limits – TIV and loss both trend
  • Losses should be on same basis as exposure rating
  • Reflective of per risk definition – READ the slip
  • Two methods to calculate burning cost
    • Empirical - weighted
    • Fit distribution
  • Split quoted layers into sub layers to add credibility
exposure rating loss curves cont d
Exposure Rating – Loss Curves (cont’d)

General Considerations

  • ELR must reflect the data underlying loss curve
  • Understanding of the data and assumptions is key
    • Assumptions of the loss curves
    • Data in exposure profiles
  • What curves to use
    • PSOLD
    • Lloyds curves
    • Salzmann curves
    • Ludwig curves
    • Curves created by reinsurers
exposure rating loss curves cont d1
Exposure Rating – Loss Curves (cont’d)
  • PSOLD
    • Becoming a standard
    • Most recent data
    • Only one that varies my AOI
    • Has the most variables
    • More on this later
  • Lloyds curves
    • Reversals exist
    • A premium calculator for facultative
    • Source unknown
  • Curves created by reinsurers
    • Old data
    • Source unknown in some cases
exposure rating loss curves cont d2
Exposure Rating – Loss Curves (cont’d)
  • Salzmann curves
    • 1960 Cov A Fire Losses Only
    • Varied by protection & construction classes
    • Not recommended by Salzmann herself
    • Use was to describe first loss scales
  • Ludwig curves
    • 1984-88 data to update the Salzmann paper
    • Based on Hartford Insurance Co. data
    • HO - all coverages, all perils
    • HO - varies by protection/construction
    • CP - small commercial data
    • CP - varies by occupancy class
exposure rating cont d
Exposure Rating (cont’d)

What is in the companies profile?

  • Limits – don’t assume, ask if unsure
    • Business interruption and/or contents included?
    • Policy limit
    • Location limit
    • PML
    • MFL
    • Key location
    • Limits or values for layered business
    • ITV issues
  • Other coverages
  • Excess policies
  • Subscription business
exposure rating cont d1
Exposure Rating (cont’d)

What is in the companies profile (cont’d)?

  • Any perils excluded?
  • Homeowners
    • Form (HO-2,3,4,5,6)
    • Coverage A only or all coverages
  • Farmowners
    • Multiple diverse buildings on a farm
    • One TIV
  • Smell test for reasonability, especially:
    • Order of magnitude of some TIV
    • Premium allocation
exposure rating psold 2004
Exposure Rating - PSOLD 2004

PSOLD

  • Data from 1992-2002
  • Can separate business by
    • Occupancy – 22 groups, diff. strongest btw.
      • Manufacturing
      • Non-manufacturing
      • HPR
      • Little differences within these groups
    • State – just distribution of business in a state
    • Gross or Net of Deductible
    • Include/Exclude Cats >$100M industry loss
    • Coverage – BGI, BGII, special, all
    • Include/Exclude WTC
    • Include/Exclude Business Interruption
exposure rating cont d2
Exposure Rating (cont’d)

Issues With PSOLD

  • Not all segments represented evenly by PSOLD
  • Loss history is thin for some groups
  • Based on 1.8M occurrences, after scrubbing
  • Losses above $5M in the database are thin
    • # of losses > $5M is 421
    • # of losses > $10M is 243
  • Refer to a list of large industry losses for more input
  • Blanket policies small amount of database
  • US business only – applicable abroad?
    • HO – US homes are built out of “cardboard”
    • Factory in US similar to one in UK?
    • Main street business in US same as France?
exposure rating cont d3
Exposure Rating (cont’d)

Application of PSOLD

  • Occupancy classes
    • 22 groups, diff. strongest btw.
      • Manufacturing
      • Non-manufacturing
      • HPR
      • Little differences within these groups
    • May need to enter TIV profile by class
      • HPR business is usually higher in limit
      • BOP type bussiness usually smaller
  • Excess Policies
  • Subscription business
exposure rating cont d4
Exposure Rating (cont’d)

Subscription and Excess Policies

  • Participation on a single layer policy
    • Insured writes 20% of a policy of 5M
    • Reinsurance layer is 500K xs 500K
    • Layer is really 25% of the loss 2.5M xs 2.5M
    • Losses above the 5M limit is not relevant to layer
  • Pure Excess Policies
    • SIRs are important
    • Limit – TIV or a hard cap
    • Blanket policies are common – allocation issues
    • 10M indivisible premium on 10 locations
exposure rating cont d5
Exposure Rating (cont’d)

Subscription Market

Layers of 50x50 and 50x100

100x50 reinsurance layer:

37.5M from 25% of 150xs350

12.5M unexposed if hard cap of 500M

50x50 reinsurance layer:

25M from 25% of 100xs250

25M from 50% of 50x200

exposure rating cont d6
Exposure Rating (cont’d)

Don’t Trust the Black Box

  • Check the output for reasonability
  • Contract Match:
    • Definition of risk
      • One building (possibly less)
      • Multiple buildings at one location
      • Entire policy
      • Company has sole determination
    • Exposure profiles
    • Loss curve
    • Dual trigger contracts – cat and risk combined
    • Scope of coverage
  • READ THE SLIP
weighting of methods
Weighting of Methods

General Considerations

  • Actual vs. Expected counts to layer (significant)
  • Actual – Needs to be adjusted for volume
  • Severity differences – may need to subdivide layer
  • Make sure that both methods reflect the same risk
  • No loss = no weight to experience? Not necessarily
  • Deficiencies in exposure data or curves
  • Past experience indicative of future
  • Do not be afraid of splitting quoted layer into parts
vendor models what to use
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
vendor models what to use cont d
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
vendor models what to use cont d1
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
vendor models what to use cont d2
Vendor Models –What to Use? (Cont’d)

Use One Model By “Territory” – An Example

vendor models what to use cont d3
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
model inputs
Model Inputs
  • Garbage In => Garbage Out
  • TIV checks/ aggregates
  • “As-if” past events
  • Scope of data (e.g. RMS – WS, EQ, TO datasets)
  • Which “territories” modeled and not modeled
  • Type of country considered for exposures abroad
  • Clash between separate zones (US – Caribbean)
unmodeled perils
“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
unmodeled perils cont d
“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
unmodeled perils cont d1
“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
    • Incorporate Risk management, esp. changes
    • No loss history – not necessarily no exposure
unmodeled perils cont d2
“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
unmodeled perils cont d3
“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
unmodeled perils cont d4
“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
unmodeled perils cont d5
“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
using the output
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
experience rating adjustments
Experience Rating – Adjustments

Reduce 80% for more credible long term experience

loadings to final el
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??????
conversion to pricing
Conversion to Pricing

General Considerations

  • Create loss distribution – even if “not needed”
  • Adjust for treaty features – AAD, swing rate, etc.
  • Understand upside and downside of deal
  • “Unpriced” capacity – blown limit, cat on tail of curve
  • Is the rate on line appropriate
  • “Red Zone” catastrophe utilization
  • Treaty correlation to book
    • Layered/Subscription business
    • Catastrophes
  • Soft Factors – Don’t be biased, though
  • Check yourself for naive capital – cheap cat cover
pro rata example
Pro Rata Example

Determining your Target Loss Ratio

key takeaways
Key Takeaways
  • Understand the data inputs
  • Understand your models and parameters
  • Understand strength and weakness of the models
  • Proper match to treaty terms – READ THE SLIP
  • Reflect true primary price
  • Rate for everything
  • Include the untested and unmodeled exposure
  • Work with your underwriter
  • Question everything – Assume nothing at face value

THINK - Don’t Just Go Through The Motions