20140825 IDRC Davos Willis Panel Esther Baur


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5th International Disaster and Risk Conference IDRC 2014 Integrative Risk Management - The role of science, technology & practice 24-28 August 2014 in Davos, Switzerland

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20140825 IDRC Davos Willis Panel Esther Baur

  1. 1. How has Catastrophe Risk Modelling, Capital Management and Regulation enhanced Reinsurers’ Resilience to Natural Disaster Risk over the last 25 Years. IDRC Davos 2014 25 August 2014 Esther Baur
  2. 2. The challenges: managing rising natural catastrophe insurance losses … IDRC Davos 2014 | Esther Baur 2 Insured losses
  3. 3. … and developing new insurance solutions to address the protection gap IDRC Davos 2014 | Esther Baur Economic vs insured losses 3 Source: sigma 1/2014
  4. 4. How have re/insurers responded to rising natural catastrophe losses over the past 25 years? • From historic risk assessment to probabilistic risk modelling • From "silos" to integrated risk management • From simple capital adequacy ratios to sophisticated risk and capital modelling and stress testing • New risk transfer mechanisms (swaps, cat bonds, etc) • Emergence of the Chief Risk Officer IDRC Davos 2014 | Esther Baur 4
  5. 5. Illustration of history of natural catastrophe assessment/modelling IDRC Davos 2014 | Esther Baur 5 Historic analysis, actuarial models, burning costs, extrapolations, typically based on Pareto Paper-based models for different exposures of portfolios, deductibles of insureds) Scenarios to calculate as-if losses First probabilistic models First IPCC report Fully probabilistic natural catastrophe models for all major perils Prior to 1980s 1980s: large storm losses 1990s: large storm losses Since 2000s Ongoing/Future Open source models Common data standards Usage outside re/insurance (private and public sector)
  6. 6. IDRC Davos 2014 | Esther Baur Four components of a natural catastrophe model What is covered? Where? How? Risk Loss resistance Value distribution Coverage conditions  Insurance sums  Limits  Excess  Exclusions  etc. How often? How strong? Example Hurricane “Charley” Aug 2004 How well built and protected? Natural catastrophe models integrate scientific, engineering, geographic and financial perspectives.
  7. 7. IDRC Davos 2014 | Esther Baur Example: Probabilistic hurricane modelling historic ~100 years probabilistic ~10‘000 years
  8. 8. Risk and capital management: stress tests to quantify potential impact of risk exposures IDRC Davos 2014 | Esther Baur 8 Swiss Re Annual Report 2013
  9. 9. Fully integrated risk and capital management: Group capital requirements based on 99% Tail VaR IDRC Davos 2014 | Esther Baur 9 Swiss Re Annual Report 2013
  10. 10. Study suggests ERM adoption reduces cost 2006 2014 GSII Reinsurers GSII Insurers of capital1 2013 US treasury creates CRO role 2012 New York appoints NY2100 Commission after Hurricane Sandy; recommendations include appointing CRO for NY state IDRC Davos 2014 | Esther Baur Evolution of the Chief Risk Officer GE Capital appoints James Law head Credit, Market & Liquidity risk: calls himself "CRO" Public Sector Private Sector 1993 S&P / AM Best disclosures establish link between ERM and Financial Strength Ratings Economist survey shows ~45% of companies have a CRO (61% in Financial 2002 Sarbanes Oxley Services) 2004 Basel II 2005 2005-6 Lehman Brothers files for bankruptcy 2009 Solvency II directive 2010 Basel III 2008 Mexico becomes first country to transfer risk to markets with securitised insurance Swiss Re appoints its first CRO 1997 Euro storms: Lothar, Martin 1999 CRO a "recent" development, embraced by enterprise and, increasingly, the public sector 10
  11. 11. • Probabilistic risk models can also be used by other industries and public sector to assess and address risks (Example: ECA studies, Government of Mexico). • Natural catastrophe risks represent contingent liabilities on balance sheets of private and public sector, which can be quantified. • Capital requirements/reserves for contingent liabilities for all industries and the public sector would provide incentives for investments in prevention and risk transfer. • A Country Risk Officer (similar to the Chief Risk Officer in the private sector) could greatly facilitate integrated risk management – across all risks and from prevention to risk transfer – in the public sector. IDRC Davos 2014 | Esther Baur Conclusion 11
  12. 12. IDRC Davos 2014 | Esther Baur Appendix 12
  13. 13. What is covered? Where? How? IDRC Davos 2014 | Esther Baur Data sources for natural catastrophe models Risk Loss resistance Value distribution Coverage conditions Exposure data from our clients How often? How strong? How well built and protected? In our models we combine data from government agencies with our own rich claims experience. National weather and earthquake services Published scientific papers Claims experience of Swiss Re Published scientific papers Exposure data from our clients. Public data sources (e.g. population density)
  14. 14. IDRC Davos 2014 | Esther Baur 14 Integrated, quantitative risk and capital management
  15. 15. IDRC Davos 2014 | Esther Baur Definition of risk tolerance 15
  16. 16. IDRC Davos 2014 | Esther Baur 16
  17. 17. IDRC Davos 2014 | Esther Baur Legal notice 17 ©2014 Swiss Re. All rights reserved. You are not permitted to create any modifications or derivative works of this presentation or to use it for commercial or other public purposes without the prior written permission of Swiss Re. The information and opinions contained in the presentation are provided as at the date of the presentation and are subject to change without notice. Although the information used was taken from reliable sources, Swiss Re does not accept any responsibility for the accuracy or comprehensiveness of the details given. All liability for the accuracy and completeness thereof or for any damage or loss resulting from the use of the information contained in this presentation is expressly excluded. Under no circumstances shall Swiss Re or its Group companies be liable for any financial or consequential loss relating to this presentation.