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Modeling Catastrophe Risk

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Over the past two decades, there has been a step-function in the scientific understanding of natural hazards, from earthquakes to hurricanes and other climatic perils. Yet most of the use cases for this knowledge have centered around the research or forecasting communities, involving highly specialized computing and scientific resources. RMS, since spinning out from Stanford University in 1989, has built its business delivering commercially relevant catastrophe modeling software and analytics to the global financial services industry, enabling business practitioners in the re/insurance sector and investors in catastrophe-linked securities to quantify, manage, and hedge their risks to these perils throughout the world.

In their talk, Hemant and Philippe discuss these business centric use cases, the modeling approaches behind them, and how the revolution in commercially available and scalable compute and analytic technologies are bringing ‘big science’ out of the lab and enabling corporations to incorporate these insights to the heart of their business processes and workflows.

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Modeling Catastrophe Risk

  1. 1. MODELING CATASTROPHE RISK Hemant Shah Co-Founder and CEO, Risk Management Solutions Philippe Stephan, CTO ©2013 Risk Management Solutions, Inc. Confidential
  2. 2. PERKINS COIE PALO ALTO, CALIFO RNIA 3150 Porter Drive Palo Alto, CA Soil Class Soil: Soft Rock to Stiff 4 3 2 Soil (2.5) Liquefaction: Very Low Landslide: Very Low DTC 4.2 Miles SaAN 100% Loss (% structure value) RMS 3 (Reinforced Concrete) 2 stories, built 1966 Approx. 35,000 ft2 10% 1% time-dependent time-independent 0% 10 100 1,000 1-in-X Probability 1 ©2013 Risk Management Solutions, Inc. Earthquake Risk @ Perkins Coie Confidential 10,000
  3. 3. Hemant and Weimin with Version 1.0, 1989 Photo from RLI Annual Report, 1990
  4. 4. OUR HISTORY ©2013 Risk Management Solutions, Inc. Confidential
  5. 5. Growing Sophistication of Models… 1,000,000 RiskLink 13 500,000+ diskettes 100,000 10,000 1,000 100 IRAS v1.0 17 diskettes 10 1 I989 2014
  6. 6. WORLDWIDE CATASTROPHE RISK MANAGEMENT – RMS MODEL COVERAGE Earthquake Tropical Cyclone Windstorm Severe Convective Storm Winter Storm Flood Terrorism Pandemic Longevity ©2013 Risk Management Solutions, Inc. Confidential
  7. 7. RISK MANAGEMENT SOLUTIONS World’s Leading Catastrophe Modeling Firm 1,300 Employees Hundreds of Clients in the Global Risk Market Subscription Revenue Business Model Individual Client Revenues from $100K - $10MM/Year Models Used to Price, Structure and Underwrite Risk; Assess and Manage Capital; and Define Mitigation Strategies ©2013 Risk Management Solutions, Inc. Confidential
  8. 8. Stochastic Event Module Hazard Module Exposure Module Vulnerability Module Financial Analysis Module
  9. 9. SIMULATING PANEUROPE FLOOD HIGH-RES OFF AND ON FLOODPLAIN CORRELATION ACROSS 13 COUNTRIES
  10. 10. Terrorist Attack Scenario A Risk Map
  11. 11. Exceedance Probability 4.0% 3.0% 2.0% 1.0% 0.0% $200M $400M $600M Loss (USD) $800M $1B
  12. 12. KEY APPLICATIONS PORTFOLIO MANAGEMENT UNDERWRITING RISK TRANSFER  Establish guidelines  Determine risk drivers  Determine reinsurance needs  Differentiate risks  Evaluate capital adequacy  Structure risk transfer  Analyze policy structures  Allocate capital  Counterparty communication  Develop pricing  Estimate losses ©2013 Risk Management Solutions, Inc. Confidential
  13. 13. Portfolio Management
  14. 14. DYNAMIC PORTFOLIO MANAGEMENT New Quoted Total New Quoted Forecast Capacity Bound Expected Renewal In-Force Time ©2013 Risk Management Solutions, Inc. Confidential
  15. 15. Drill down into your book View a multitude of metrics all in one place
  16. 16. Interact with multiple EP curves Investigate the drivers of change
  17. 17. Risk Transfer | Cat Bonds
  18. 18. ©2013 Risk Management Solutions, Inc. Confidential
  19. 19. 1.7% ATTACHMENT PROBABILITY 8½ft AT THE BATTERY. AS SIMPLE AS THAT.
  20. 20. CASE STUDY: METROCAT RE 2013-1 ©2013 Risk Management Solutions, Inc. Confidential
  21. 21. “ The innovative non-traditional structure allowed MTA to close it’s storm surge insurance gap Non-traditional deal of the year Bond Buyer magazine
  22. 22. METRO CAT BOND IN THE NEWS ©2013 Risk Management Solutions, Inc. Confidential
  23. 23. Supply Chain Risk
  24. 24. Tohoku Earthquake 2011 caused supply disruption Major damage: Renesas (40% market share of MCU) GLOBAL SUPPLY CHAIN Explosion in Germany 2012 caused supply disruption Evonik damaged 50% market share of Cyclododecatriene(CDT) Thailand Flood 2011 caused supply disruption Major production hub is damaged (25% of computer hard drives in the world) Material Suppliers ©2013 Risk Management Solutions, Inc. Hurricane Sandy 2012 caused disruption of distribution centers Confidential Parts Suppliers Facility Distributions
  25. 25. EXAMPLE SUPPLY-CHAIN NETWORK IN AUTO INDUSTRY High Tech Parts Severe damage in Tohoku area Domestic Distribution Gears Engine Assembling Global Distribution Metal Forging Transmission Suppliers (Parts) ©2013 Risk Management Solutions, Inc. Confidential Suppliers (Parts) Facility Distributions
  26. 26. Network Topology and Conceptual Model EXAMPLE SUPPLY-CHAIN NETWORK Analytical Model Loss Model CBI Simulation Engine ©2013 Risk Management Solutions, Inc. Confidential
  27. 27. THE TECHNOLOGY SIDE Hive talk February 5th, 2014 Philippe Stephan, CTO ©2013 Risk Management Solutions, Inc. Confidential
  28. 28. Key questions users ask How much for this risk? How is my portfolio? What if something changed?
  29. 29. How we get to answers Event D,σ $ Damage Location T&C Event Intensity Contract
  30. 30. Events are compile-time objects
  31. 31. Scale In : 1 portfolio ≡ 1Gb of client data Out: 1 model run ≡ 5T (* 8bytes) = 40Tb 50K events 100K locations 1K damage samples => Big Re. co: 5K portfolio ≡ 200Pb
  32. 32. Complexity Non additivity of risk Multiple what-ifs Regulatory framework (keep, encrypt, audit)
  33. 33. 100% CPU versus memory access 90% % time in MEX 80% 70% 60% 50% 40% 30% 0:00:00 0:07:12 0:14:24 0:21:36 0:28:48 MEX/BI Workflow Duration (mins) 0:36:00 0:43:12 0:50:24
  34. 34. 0.700 … only realizable in the cloud 0.650 0.600 Exceedance Probability 0.550 0.500 0.450 0.400 0.350 0.300 0.250 0.200 0.150 0.100 0.050 - 20,000 25,000 30,000 35,000 40,000 Max Cores 45,000 50,000 55,000 60,000
  35. 35. Our stack
  36. 36. What’s next for RMS(one) A reference database of subjects at risk An extensible exposure mgt system An ecosystem of models A generic risk exploration system A communication platform
  37. 37. Hi from the RMS Sr. Management Team 37

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