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  • Soil map area is about 5 miles E-W by 4.6 N-SDistance to faults: 4.2 miles to the San Andreas (SW)2 miles to Monte Vista-Shannon fault (SW). This one is a little dubious as to its activity level, however, so best to focus on SAFZ.15 miles to Hayward (NE)Area of better soils to the southeast on the map are the low hills near their office. Site photo from
  • RMS catastrophe models incorporate the latest science, over 20 years of dedicated catastrophe risk research and development, and partnerships with local institutions and leading regional experts. Catastrophe events are generated using RMS supercomputers and advanced numerical modeling on global simulation platforms.We are committed to providing our clients with the best level of product support and client service. RMS clients have access to a wide array of resources and expertise, from dedicated account representatives, to scientific and technical experts, to unparalleled training and documentation.
  • Navigating the Risk Landscape for Over 20 Years
  • Europe flood is one of the first models that will showcase the benefits of HD-simulation. This video a simulation of the weather over Europe over the course of several days. Blue colors indicate areas of high rainfall.Flood modeling at high-resolution, on such a large scale is not possible on any of todays platforms. The RMS europe flood model will cover 13 countries, on a single event set, allowing you to assess correlation between risks that are far away from each other. This matters, because of the length of the european rivers, which can flow through multiple countries. By starting with rainfall, and modeling how that rainfall transforms to runoff and moves across the landscape, including the impacts of topography, soil type, temperature, season, evaporation, allows us to model the probability of flooding from rivers as well as flash floods, and the impact of snowfall and snowmelt, in summer and in winter, and account for potential clustering of events when the jet stream gets locked in place.The model can be used for both portfolio management and underwriting individual risks. With the time-based simulation, you can model important policies such as the hours clause, properly, by defining your own time windows relative to the event losses through time.
  • Underwriting: Field offices enter exposure data and review account analysis using RiskBrowser web interface. Underwriter investigates available capacity and prices risk. Central database is updated with exposure and account level results.Portfolio Management: Using RiskLink, home office performs analysis of portfolio exposure to identify key accumulations, allocate capacity, determine reinsurance needs, and benchmark risk against capital requirements.Risk Transfer: Brokers work with home office to structure reinsurance program and prepare exposure data. Reinsurers use RiskLink to price risk using detailed or aggregated exposure data and industry benchmarks.
  • Dynamic portfolio management – A Key aspect of RMS(one)
  • Drill down into your book in real time by region, line of business, legal entity, and other customizable hierarchiesView a multitude of metrics (including exposure, modeled losses, profitability indicators, benchmarks, and capacity utilization) all in one place and investigate key drivers of risk and change
  • Interact with multiple EP (exceedance probability) curves to gain intuition around the events and contracts that drive different parts of the curve for a given portfolio and the associated sensitivitiesCompare EP curves and investigate the drivers of change between different points in time
  • Propose changing the format of this case study section – frame it as Problem & SolutionThis is the problem slideBadly hit in sandy (the image should say this)Problem criteria wereNeed Efficient cover, having failed to find capacity in the reinsurance marketSimple index in order that investors could take the risk without having to underwrite the complex portfolioSpeed of settlement (need a third one – any better ideas?!)
  • Metrocat triggered by simple coastal flood measurements at tide gauges in the NYC area. A very simple trigger, but required the most advanced coastal flood modelling to make this possible.
  • MTA found that capacity dried up in the reinsurance market after sandy. They needed an efficient way to access capital, and found the ILS market ready to step in, using a parametric index. The deal won the Bond Buyer Magazine non-traditional deal of the year.
  • Navigating the Risk Landscape for Over 20 Years
  • Cyclododecatriene (CDT) is a key component in a nylon resin called PA12, which is used to make a specialized plastic.The plastic is used in auto fuel lines and brake lines. It is also a component in solar cell, pipelines, sporting good and household items.Evonik accounts for about 50 percent of the world’s CDT production, which estimated 40% of the world's PA12 capacity (CDT) is a key component in a nylon resin called PA12, which is used to make a specialized plastic.The plastic is used in auto fuel lines and brake lines. It is also a component in solar cell, pipelines, sporting good and household items.PA12 has been in short supply for about two years, as demand from the solar industry increased. The Evonik incident will worsen the shortage, which could hit every major automaker, Sharland said, executive director of the Automotive Industry Action Group., which is weighing an initial public offering of its stock by July 1, accounts for about 50 percent of the world’s CDT production, Ube’s Sumiyoshi said. The PA-11 resin can be used as a partial substitute for PA-12, Sumiyoshi said. blast at Evonik Industries' cyclododecatriene (CDT) plant in Marl, Germany, has taken out an estimated 40% of the world's nylon 12 (also known as polyamide 12, or PA-12) capacity, leaving automotive customers scrambling to find alternatives. Now producers of polyamides are working with automakers to fill the void with alternatives.
  • Transcript

    • 1. MODELING CATASTROPHE RISK Hemant Shah Co-Founder and CEO, Risk Management Solutions Philippe Stephan, CTO ©2013 Risk Management Solutions, Inc. Confidential
    • 2. PERKINS COIE PALO ALTO, CALIFORNIA 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. Hemant and Weimin with Version 1.0, 1989 Photo from RLI Annual Report, 1990
    • 4. OUR HISTORY ©2013 Risk Management Solutions, Inc. Confidential
    • 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. 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. 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. Stochastic Event Module Hazard Module Exposure Module Vulnerability Module Financial Analysis Module
    • 10. Terrorist Attack Scenario A Risk Map
    • 11. Exceedance Probability 4.0% 3.0% 2.0% 1.0% 0.0% $200M $400M $600M Loss (USD) $800M $1B
    • 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. Portfolio Management
    • 14. DYNAMIC PORTFOLIO MANAGEMENT New Quoted Total New Quoted Forecast Capacity Bound Expected Renewal In-Force Time ©2013 Risk Management Solutions, Inc. Confidential
    • 15. Drill down into your book View a multitude of metrics all in one place
    • 16. Interact with multiple EP curves Investigate the drivers of change
    • 17. Risk Transfer | Cat Bonds
    • 18. ©2013 Risk Management Solutions, Inc. Confidential
    • 20. CASE STUDY: METROCAT RE 2013-1 ©2013 Risk Management Solutions, Inc. Confidential
    • 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. METRO CAT BOND IN THE NEWS ©2013 Risk Management Solutions, Inc. Confidential
    • 23. Supply Chain Risk
    • 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. 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. Network Topology and Conceptual Model EXAMPLE SUPPLY-CHAIN NETWORK Analytical Model Loss Model CBI Simulation Engine ©2013 Risk Management Solutions, Inc. Confidential
    • 27. THE TECHNOLOGY SIDE Hive talk February 5th, 2014 Philippe Stephan, CTO ©2013 Risk Management Solutions, Inc. Confidential
    • 28. Key questions users ask How much for this risk? How is my portfolio? What if something changed?
    • 29. How we get to answers Event D,σ $ Damage Location T&C Event Intensity Contract
    • 30. Events are compile-time objects
    • 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. Complexity Non additivity of risk Multiple what-ifs Regulatory framework (keep, encrypt, audit)
    • 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. 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. Our stack
    • 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. Hi from the RMS Sr. Management Team 37