Catastrophe Modeling SessionCatastrophe Modeling SessionReinsurance Boot CampReinsurance Boot Camp
August 10, 2009August 10, 2009
Aleeza Cooperman Serafin
Guy Carpenter & Co, LLC
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The Black Box
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Cat Modeling
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Presentation Outline
What are catastrophe models?
How do catastrophe models work?
Cat modeling process
Understanding model output
How is model output used?
Questions - throughout
What are Cat Models?
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Catastrophe Modeling and Model Vendors
What?–A tool that quantifies risk
How?–Examines insured values that are exposed to catastrophic perils such as hurricanes, earthquakes and terrorism
Why?–Aids management decision making on
Pricing and underwriting Reinsurance buying Rating Agencies Portfolio management
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Catastrophe Model Vendors
• Founded in 1987
• Pioneered the probabilistic catastrophe modeling technology
• Founded at Stanford University in 1988
• World's leading provider of products and services for the quantification and management of catastrophe risks.
• Grew in the 1990s, expanding services and perils covered.
• Founded in 1980s
• One of first catastrophe models in industry
Other models
• Most large reinsurers and other risk management companies have developed their own in-house models
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Modeled Perils
Hurricane– Wind and rain– Demand Surge (Loss Amplification) and Storm Surge
Earthquake– Shake– Fire Following– Demand Surge and Sprinkler Leakage
Other wind
Winter storm
Terrorism
Flood (Europe)
Wildfire
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Deterministic Model–Modeling using a single
discrete event–The event is assumed to
happen without regard to probability
–Commonly seen as recreations of historic events or single- hypothetical analysis
Probabilistic Model–Uses a series of
simulated events and accounts for the probability of those events over time
Types of Models
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Modeled Lines of Business
Personal lines property
Commercial lines property
Industrial property
Builders Risk
Marine
Auto physical damage (Personal Auto)
Workers compensation
Lives at risk – Accident and Health
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Building/Vessel/Vehicle
– Other structures
Contents
– Stock
– Machinery
– Inland marine
– Marine
Time Element
– Business Interruption
– Loss of Use
Head Count
Payroll
Modeled Coverages
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FTP Site – used to transfer files to clients & markets
Transmittal Document – includes instructions for accessing the FTP site, lists what files are posted and explains what’s in them
EDM – RMS-specific database containing exposures
RDM – RMS-specific database containing analysis results
CEDE – AIR-specific database containing exposures
CLF – AIR-specific file containing detailed analysis results. Can be loaded into CATRADER in order to apply cat treaties.
Unicede – Text file containing aggregate (by county) exposure information by line of business, includes TIVs by county, no individual location detail. Used in AIR CATRADER (can be used in RMS) to perform aggregate analysis.
Post Import Summary (PISR) – RMS report summarizing exposures in a portfolio (TIV, count, geocoding, etc.)
Catastrophe Modeling Terminology
How do Cat Models work?Understanding the Black Box
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Hazard Module
Engineering Module
Financial Module
Defines the Event
Vulnerability of the Structure
Loss Calculation
Portfolio Definition
Insurer Location and Policy Inputs
The Four Catastrophe Model ComponentsThe Black Box
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2
3
4
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Module 1 – Portfolio DefinitionInputs
Formatted exposure data – Coverages– Terms– Risk characteristics– Reinsurance
Spatial Lookups– Geocoding– Hazard
Hurricane: Distance to Coast, Elevation Earthquake: Soil type
Portfolio Definition1
Hazard2
Engineering3
Financial4
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Module 1 – Portfolio Definition Data Quality
Completeness
Correctness– Construction, occupancy, etc– Location information– Values
Valuation date– Current– Reflecting growth or reduction
Sources of uncertainty– Entry errors– Old records– Miscoding
Portfolio Definition1
Hazard2
Engineering3
Financial4
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2
1
4
3
Individual risk locations
Geocoding – geographic recognition
Module 1 – Portfolio Definition Geocoding
Geocoding Resolution
County Worst
City
Zip Code
Street
Lat / Long Best
Portfolio Definition1
Hazard2
Engineering3
Financial4
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Requirements– Geocoding: latitude and longitude coordinates
Based on address information – Geospatial information: environmental and/or physical factors that can
influence an event’s intensity at the site Soil conditions Topography and surface roughness Adjacent buildings
Module 2 – HazardPortfolio Definition1
Hazard2
Engineering3
Financial4 Generates the physical disturbance that is produced by an event
– Hurricane: Site Wind Speed– Earthquake: Ground Motion – Tornado/ Hail: Event Intensity
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Module 2 – Hazard DefinitionHurricane Example
Portfolio Definition1
Hazard2
Engineering3
Financial4
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Module 2 – Hazard DefinitionHurricane Example - Stochastic Database
Thousands of hypothetical events
Windstorm Parameters• Central Pressure• Radius to Max. Wind• Translational Speed• Wind Profile• Fill Rate• Terrain, etc.
Portfolio Definition1
Hazard2
Engineering3
Financial4
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Module 2 – Hazard DefinitionHurricane Example - Event Rates
Last 100 years of historical data averages about 2.4 landfalling events per year
Traditional event probabilities distributed among thousands of storms
Portfolio Definition1
Hazard2
Engineering3
Financial4 Each stochastic event is assigned a rate – an annual frequency
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Near-term hurricane frequency
Five year view (RMS)
More than three landfalling events per year
Module 2 – Hazard DefinitionHurricane Example - Event Rates
Portfolio Definition1
Hazard2
Engineering3
Financial4
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2
1
4
3
Compute wind speed at each risk location
Vw = f(Pc, d, regional topography)
Distance (d)
Hurricane
Path
Module 2 – Hazard DefinitionHurricane Example - Calculate Site Windspeed
Portfolio Definition1
Hazard2
Engineering3
Financial4
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• Frequency of earthquakes• Fault location• Fault geometry:
• length• depth• strike angle• dip angle
• Magnitude-recurrence• Soil type
Rupture length
Fault
Epicenter
Module 2 – Hazard DefinitionEarthquake Example - Site Ground Motion
Portfolio Definition1
Hazard2
Engineering3
Financial4
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Major sources of uncertainty:– Limited historical data on events– Unknown atmospheric elements may not be recognized e.g.
Hurricane cycles
Module 2 – Hazard DefinitionLimitations
Portfolio Definition1
Hazard2
Engineering3
Financial4
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Data Required:– Value
What is the value of the insured property?– Occupancy
How is the property used?- Residential
Single or Multi-Family- Commercial
Mercantile or Industrial– Construction
How is the property constructed?- Frame, Masonry, Metal, etc.- Lowrise or Highrise
– Age When was the property built? What building codes apply?
Module 3 – Vulnerability DefinitionPortfolio Definition1
Hazard2
Engineering3
Financial4
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Damaged Properties
1
4
Hurricane
Module 3 – Vulnerability
Building Damageability
0%
20%
40%
60%
80%
100%
70 90 110 130 150Wind Speed
Dam
ag
e
Const 1 Const 2 Const 3
Frame Construction
50%
Damage Rates are for illustration only and are not selected from any particular model
Portfolio Definition1
Hazard2
Engineering3
Financial4
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Major sources of uncertainty:– Limited claims data– Improper coding of risk characteristics– Lack of understanding of structural behavior under severe loads
Module 3 – VulnerabilityLimitations
Portfolio Definition1
Hazard2
Engineering3
Financial4
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Evaluates multiple financial perspectives–Ground up: damage prior to coverage limits and
deductibles–Gross: loss after deductibles, limits, attachment points–Net: loss after treaty cessions, facultative, etc.
Module 4 –Financial Perspectives
Decreasing loss levels
Portfolio Definition1
Hazard2
Engineering3
Financial4Calculates insured losses given the
damage level and user risk inputs
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Major sources of uncertainty:– Limits versus Value at Risk– Insurance and reinsurance structures are applied to loss distribution
differently: Site-level loss Policy-level loss
Module 4 – Financial PerspectivesLimitations
Portfolio Definition1
Hazard2
Engineering3
Financial4
Catastrophe Modeling Process
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The Catastrophe Modeling ProcessOverview
Determine project scope
Gather relevant data
Evaluate, verify and format data– Data quality checklist– Data assumptions document
Import
Run the model
Review the output
Extract detailed losses
Present results
Post analysis portfolio management
Understanding Model Output
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Model OutputTerminology
Average Annual Loss (aka Pure Premium, aka Expected Loss): Long term average loss expected in any one year
OEP - Occurrence Exceeding Probability: Probability that a single occurrence will exceed a certain threshold
AEP - Aggregate Exceeding Probability: Probability that one or more occurrences will
combine in a year to exceed the threshold.
Return Period: Level of loss and the expected amount of time between recurrences.
Critical Prob. Return Period AEP Loss OEP Loss
0.10% 1,000 160 147
0.20% 500 144 134
0.40% 250 126 118
0.50% 200 120 112
1.00% 100 97 90
Pure Premium 8
Standard Deviation 18
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Model OutputThe Event Loss Table
Sample Event Output:
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Model OutputThe Event Loss Table – determining PMLs - OEP
Different levels of severity based on company appetite
Common to monitor portfolios “1-100 year” loss level
In the example, “100 year loss level” is saying that there is a 1% chance that there will be a single occurrence of $2.5 billion or greater in any given year
Event IDEvent Prob
Loss Accum Prob
437812 0.05% 4,400,000,000 0.05%
437830 0.10% 4,000,000,000 0.10%
438632 0.35% 3,750,000,000 0.45%
437676 0.30% 3,600,000,000 0.75%
438622 0.10% 2,750,000,000 0.85%
438489 0.15% 2,500,000,000 1.00%
438451 0.70% 2,400,000,000 1.70%
438351 0.80% 2,250,000,000 2.50%
438248 0.90% 2,240,000,000 3.40%
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Model OutputThe Event Loss Table – AEP
AEP reflects year’s worth of events rather than a single event – i.e. “there is an X% chance that there will be a total of $XX billion or
greater losses in total in any given year”
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Model OutputThe Event Loss Table – determining average annual loss
Average annual loss is the weighted average of the event losses and their likelihood of occurring
A company should collect at least $91million in CAT premium to cover its average annual expected loss for the peril and portfolio being modeled
Event IDEvent Prob
Loss
437812 0.05% 4,400,000,000
437830 0.10% 4,000,000,000
438632 0.35% 3,750,000,000
437676 0.30% 3,600,000,000
438622 0.10% 2,750,000,000
438489 0.15% 2,500,000,000
438451 0.70% 2,400,000,000
438351 0.80% 2,250,000,000
438248 0.90% 2,240,000,000
Sum Product of Event Probability
and Loss
=
$91M
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Average Annual LossProperties
AAL used to determine loss drivers:– Territory
Zip code County State Rating territory
– Source Risk location Policy Product line Producer
– Characteristics Construction class Occupancy
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Understanding Model Uncertainty
Primary Uncertainty - Uncertainty in the occurrence of an event
Secondary Uncertainty - Uncertainty in the loss level–Range of possible loss levels
“Inherent” uncertainty–Uncertainty in the vulnerability (damage) driven by:
Insufficient historical data (infrequent) Poor quality data Translating data from one region to the next (San
Francisco 1906)
How is Catastrophe Model Output Used?
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Catastrophe Model OutputPortfolio Management - Monitoring Loss/Premium Ratio in RML
Excluding 685 policies
from portfolio produces
an optimal RML/Premium ratio
Risk Managed Layer (RML): a range of loss levels from the EP Curve that the company wants to manage
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Index Range # ZipCodes
Top 10 ZipCodes: ZipCode Index
County Name ZipCode IndexMiami-Dade 33149 1.0000
Miami-Dade 33109 0.9946
Miami-Dade 33131 0.8472
Martin 34958 0.8230
Miami-Dade 33231 0.8195
Miami-Dade 33121 0.7317
Miami-Dade 33159 0.7283
Miami-Dade 33119 0.7237
Miami-Dade 33245 0.6882
Martin 34996 0.6754
Catastrophe Model OutputGradient Map – Zip Code Index
Identifies how geographic areas are correlated to show growth/reduction opportunities
Reveals the most critical geographic areas contributing loss to the RML
Shows relative contribution to RML losses by Zip Code.
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Earthquake
Severe WeatherHurricaneWildfire
Tornado/HailFlood
Catastrophe Model OutputReal-time event monitoring
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ConclusionsCatastrophe Model Benefits and Shortcomings
Values– Valuable risk measure– Encourage better data tracking– Create marketplace advantages– Innovation
Dangers– Over-reliance– Misuse– Errors
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Questions?
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Disclaimer
The data and analysis provided by Guy Carpenter herein or in connection herewith are provided “as is”, without warranty of any kind whether express or implied. Neither Guy Carpenter, its affiliates nor their officers, directors, agents, modelers, or subcontractors (collectively, “Providers”) guarantee or warrant the correctness, completeness, currentness, merchantability, or fitness for a particular purpose of such data and analysis. In no event will any Provider be liable for loss of profits or any other indirect, special, incidental and/or consequential damage of any kind howsoever incurred or designated, arising from any use of the data and analysis provided herein or in connection herewith.
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