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Catastrophe Modeling Session Reinsurance Boot Camp

August 10, 2009. Catastrophe Modeling Session Reinsurance Boot Camp. Aleeza Cooperman Serafin Guy Carpenter & Co, LLC. The Black Box. Cat Modeling. Presentation Outline. What are catastrophe models? How do catastrophe models work? Cat modeling process Understanding model output

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Catastrophe Modeling Session Reinsurance Boot Camp

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  1. August 10, 2009 Catastrophe Modeling SessionReinsurance Boot Camp Aleeza Cooperman Serafin Guy Carpenter & Co, LLC

  2. The Black Box

  3. Cat Modeling

  4. Presentation Outline • What are catastrophe models? • How do catastrophe models work? • Cat modeling process • Understanding model output • How is model output used? • Questions - throughout

  5. What are Cat Models?

  6. 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

  7. Catastrophe Model Vendors • 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 1987 • Pioneered the probabilistic catastrophe modeling technology • 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

  8. 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

  9. 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

  10. 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

  11. Modeled Coverages • Building/Vessel/Vehicle • Other structures • Contents • Stock • Machinery • Inland marine • Marine • Time Element • Business Interruption • Loss of Use • Head Count • Payroll

  12. 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

  13. How do Cat Models work? Understanding the Black Box

  14. The Four Catastrophe Model ComponentsThe Black Box Insurer Location and Policy Inputs Portfolio Definition 1 Defines the Event HazardModule 2 Vulnerability of the Structure EngineeringModule 3 Loss Calculation FinancialModule 4

  15. Portfolio Definition 1 Module 1 – Portfolio DefinitionInputs Hazard 2 Engineering 3 • Formatted exposure data • Coverages • Terms • Risk characteristics • Reinsurance • Spatial Lookups • Geocoding • Hazard • Hurricane: Distance to Coast, Elevation • Earthquake: Soil type Financial 4

  16. Portfolio Definition 1 Module 1 – Portfolio Definition Data Quality Hazard 2 Engineering 3 • Completeness • Correctness • Construction, occupancy, etc • Location information • Values • Valuation date • Current • Reflecting growth or reduction • Sources of uncertainty • Entry errors • Old records • Miscoding Financial 4

  17. Portfolio Definition 1 Module 1 – Portfolio DefinitionGeocoding Hazard 2 Engineering 3 Financial 4 Individual risk locations 2 1 3 Geocoding – geographic recognition 4

  18. Portfolio Definition 1 Module 2 – Hazard Hazard 2 Engineering 3 • 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 • Generates the physical disturbance that is produced by an event • Hurricane: Site Wind Speed • Earthquake: Ground Motion • Tornado/ Hail: Event Intensity Financial 4

  19. Module 2 – Hazard DefinitionHurricane Example Portfolio Definition 1 Hazard 2 Engineering 3 Financial 4

  20. Portfolio Definition 1 Module 2 – Hazard DefinitionHurricane Example - Stochastic Database Hazard 2 Engineering 3 Thousands of hypothetical events Financial 4 • Windstorm Parameters • Central Pressure • Radius to Max. Wind • Translational Speed • Wind Profile • Fill Rate • Terrain, etc.

  21. Portfolio Definition 1 Module 2 – Hazard DefinitionHurricane Example - Event Rates Hazard 2 Engineering 3 • Each stochastic event is assigned a rate – an annual frequency Financial 4 • Last 100 years of historical data averages about 2.4 landfalling events per year • Traditional event probabilities distributed among thousands of storms

  22. Portfolio Definition 1 Module 2 – Hazard DefinitionHurricane Example - Event Rates Hazard 2 Engineering 3 • Near-term hurricane frequency • Five year view (RMS) • More than three landfalling events per year Financial 4

  23. Portfolio Definition 1 Module 2 – Hazard DefinitionHurricane Example - Calculate Site Windspeed Hazard 2 Engineering 3 Financial 4 Path 2 Distance (d) Compute wind speed at each risk location Vw = f(Pc, d, regional topography) 1 3 4 Hurricane

  24. Portfolio Definition 1 Module 2 – Hazard DefinitionEarthquake Example - Site Ground Motion Hazard 2 Engineering 3 Financial 4 • Frequency of earthquakes • Fault location • Fault geometry: • length • depth • strike angle • dip angle • Magnitude-recurrence • Soil type Epicenter Rupture length Fault

  25. Portfolio Definition 1 Module 2 – Hazard DefinitionLimitations Hazard 2 Engineering 3 • Major sources of uncertainty: • Limited historical data on events • Unknown atmospheric elements may not be recognized e.g. Hurricane cycles Financial 4

  26. Portfolio Definition 1 Module 3 – Vulnerability Definition Hazard 2 Engineering 3 • 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? Financial 4

  27. Building Damageability 100% 80% 60% Damage 40% 20% 0% 70 90 110 130 150 Wind Speed Const 3 Const 1 Const 2 Portfolio Definition 1 Module 3 – Vulnerability Hazard 2 Engineering 3 Financial 4 Frame Construction 50% 1 4 Damaged Properties Hurricane Damage Rates are for illustration only and are not selected from any particular model

  28. Portfolio Definition 1 Module 3 – VulnerabilityLimitations Hazard 2 Engineering 3 • Major sources of uncertainty: • Limited claims data • Improper coding of risk characteristics • Lack of understanding of structural behavior under severe loads Financial 4

  29. Portfolio Definition 1 Module 4 –Financial Perspectives Hazard 2 Engineering 3 • 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. • Calculates insured losses given the damage level and user risk inputs Financial 4 Decreasing loss levels

  30. Portfolio Definition 1 Module 4 – Financial PerspectivesLimitations Hazard 2 Engineering 3 • 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 Financial 4

  31. Catastrophe Modeling Process

  32. 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

  33. Understanding Model Output

  34. 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.

  35. Sample Event Output: Model OutputThe Event Loss Table

  36. 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 Model OutputThe Event Loss Table – determining PMLs - OEP

  37. 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” Model OutputThe Event Loss Table – AEP

  38. 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 Model OutputThe Event Loss Table – determining average annual loss Sum Product of Event Probability and Loss = $91M

  39. AAL used to determine loss drivers: Territory Zip code County State Rating territory Source Risk location Policy Product line Producer Characteristics Construction class Occupancy Average Annual LossProperties

  40. 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)

  41. How is Catastrophe Model Output Used?

  42. Catastrophe Model OutputPortfolio Management - Monitoring Loss/Premium Ratio in RML Risk Managed Layer (RML): a range of loss levels from the EP Curve that the company wants to manage Excluding 685 policies from portfolio produces an optimal RML/Premium ratio

  43. Catastrophe Model OutputGradient Map – Zip Code Index # ZipCodes Index Range • 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. Top 10 ZipCodes: ZipCode Index

  44. Catastrophe Model OutputReal-time event monitoring Wildfire Hurricane Severe Weather Flood Tornado/Hail Earthquake

  45. ConclusionsCatastrophe Model Benefits and Shortcomings • Values • Valuable risk measure • Encourage better data tracking • Create marketplace advantages • Innovation • Dangers • Over-reliance • Misuse • Errors

  46. Questions?

  47. 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.

  48. www.guycarp.com

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