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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 - PowerPoint PPT Presentation
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Property Reinsurance Ratemaking
Sean DevlinReinsurance Boot Camp on Pricing Techniques July 29, 2005
2GE Insurance Solutions
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 Summary and Questions
3GE Insurance Solutions
July 29, 2005
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
4GE Insurance Solutions
July 29, 2005
Exposure Rating
5GE Insurance Solutions
July 29, 2005
ELR DeterminationFoundation 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
6GE Insurance Solutions
July 29, 2005
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
7GE Insurance Solutions
July 29, 2005
ELR DeterminationTrend 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
8GE Insurance Solutions
July 29, 2005
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
9GE Insurance Solutions
July 29, 2005
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
10GE Insurance Solutions
July 29, 2005
Primary “Price” (cont’d)
Rate Adequacy Over Time
Time
Ind
ex
Regional
Specialty
National
11GE Insurance Solutions
July 29, 2005
Primary “Price” (cont’d)
True Price vs Captured Price
Time
Ind
ex
Price Monitor
Actual Price
“Uncaptured” Rate change
12GE Insurance Solutions
July 29, 2005
Primary “Price” (cont’d)Price Assumption Effects on Cal Yr Results
Time/Year
Lo
ss R
ati
o
Plan
Actual
Cal Yr
Calendar Year results understated during soft market
Actual peak of soft market
Should be hardening here
13GE Insurance Solutions
July 29, 2005
Exposure and Experience Rating
14GE Insurance Solutions
July 29, 2005
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
15GE Insurance Solutions
July 29, 2005
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
16GE Insurance Solutions
July 29, 2005
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
17GE Insurance Solutions
July 29, 2005
Exposure Rating – Loss Curves (cont’d)
Salzmann curves1960 Cov A Fire Losses Only Varied by protection & construction classes Not recommended by Salzmann herself Use was to describe first loss scales
Ludwig curves1984-88 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
18GE Insurance Solutions
July 29, 2005
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
19GE Insurance Solutions
July 29, 2005
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
20GE Insurance Solutions
July 29, 2005
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
21GE Insurance Solutions
July 29, 2005
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?
22GE Insurance Solutions
July 29, 2005
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
23GE Insurance Solutions
July 29, 2005
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
24GE Insurance Solutions
July 29, 2005
Exposure Rating (cont’d)
Subscription MarketLayers of 50x50 and 50x100500M
25% of250x250
250M
100M50% of 150x100
100M SIR
50x50 reinsurance layer:25M from 25% of 100xs25025M from 50% of 50x200
100x50 reinsurance layer:37.5M from 25% of 150xs350
12.5M unexposed if hard cap of 500M
25GE Insurance Solutions
July 29, 2005
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
26GE Insurance Solutions
July 29, 2005
Weighting of MethodsGeneral 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
27GE Insurance Solutions
July 29, 2005
Catastrophe PerilPer RiskPro RataCat XL
28GE Insurance Solutions
July 29, 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
29GE Insurance Solutions
July 29, 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
30GE Insurance Solutions
July 29, 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
31GE Insurance Solutions
July 29, 2005
Vendor Models –What to Use? (Cont’d)
Use One Model By “Territory” – An Example 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 70% 150% 130%Japan EQ 80% 120% 120%FL WS 90% 100% 75%Euro Wind 150% 85% 75%
32GE Insurance Solutions
July 29, 2005
Vendor Models –What to Use? (Cont’d)
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
33GE Insurance Solutions
July 29, 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 “territories” modeled and not modeled Type of country considered for exposures abroad Clash between separate zones (US – Caribbean)
34GE Insurance Solutions
July 29, 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
35GE Insurance Solutions
July 29, 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
36GE Insurance Solutions
July 29, 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 Incorporate Risk management, esp. changes No loss history – not necessarily no exposure
37GE Insurance Solutions
July 29, 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
38GE Insurance Solutions
July 29, 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
39GE Insurance Solutions
July 29, 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
40GE Insurance Solutions
July 29, 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
41GE Insurance Solutions
July 29, 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
42GE Insurance Solutions
July 29, 2005
Experience Rating – AdjustmentsTrended Volume
Year Exposure Loss Adj Loss1990 1,000 500 2,0891991 1,100 0 01992 1,210 2,000 6,9051993 1,331 0 01994 1,464 0 01995 1,611 0 01996 1,772 5,000 11,7901997 1,949 0 01998 2,144 0 01999 2,358 0 02000 2,594 2,000 3,2212001 2,853 0 02002 3,138 0 02003 3,452 900 1,0892004 3,797 0 02005 4,177 0 0
Average 650 1,568
Industry 30 yr 80,000Industry (90-04) 100,000
Adjusted 1,255
Reduce 80% for more credible long term experience
43GE Insurance Solutions
July 29, 2005
Loadings to final ELConsiderations in final indicated “price”
% of loss? % of ? 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??????
44GE Insurance Solutions
July 29, 2005
Capacity Charge - Simplistic
5,000
5,000
0
3,000
3,000
0
600
600
800
250
250
3,500
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PremiumComponents$000s
50x50 100x100 100x200 200x300Layer
$M
Indicated vs. Capacity
Capacity
Load
EL
45GE Insurance Solutions
July 29, 2005
Conversion to PricingGeneral 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
46GE Insurance Solutions
July 29, 2005
Finishing The Job
47GE Insurance Solutions
July 29, 2005
Pro Rata ExampleDetermining your Target Loss Ratio
Loss Ratio Loading Total Example/Comments30.0% 2.5% 32.5% First 100K per risk10.0% 2.0% 12.0% unl xs 100k per risk10.0% 2.0% 12.0% Thunderstorm/Tordano/Hail2.0% 0.5% 2.5% Winterstorm/Wildfire5.0% 5.0% 10.0% Hurricane/EQ
30.0% 0.0% 30.0% Could be negative load for slide87.0% 12.0% 99.0% Total Should be less than 100%
48GE Insurance Solutions
July 29, 2005
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