Use of Cat-Multi-Models for the Insurance Industry

1. Use of Cat-Multi-Models for the Insurance Industry Gero Michel

2. Conflicting Objectives: • Commercial strategy: based on generating value short (to medium) term • Inter-annual Variability: Many opportunities might not be profitable for one year • Diversification: The insurance world is too small to diversify cat risk away • History based: might not be sufficient to forecast the future • Short-term needs: Cat Models are in general long-term • “Accountable”: Avoid the outsized loss? • “Opportunity”: Outsource your brain to the consensus?

3. Why is there so little interest in analytics/ERM in our market?

4. Risk Tolerance: • Four types of companies: • Risk Averse • Risk Taking • Analytical/Managing • Pragmatists

5. Only the Analysts and Pragmatists might want to use models (top-down or bottom up) but Only the Analysts consider Multi- Modeling necessary (without being further incentivized)

6. Solvency II/Regulator: Likely to ask for Multi-Modeling/Near Term

7. Catastrophe Models: Defined EL for Almost any Stretch, Peril & Territory 10,000 yrs statistical 300 yrs GCM “Trials of Stochastic event sets” limited by “ knowledge, computer power, and imagination”,

8. Collectively induced: Models are rejected in case they do not match expectation • Believe:The “big thumb” is as good as any one model • Value of model: lies in the disaggregation of risk, Pricing and Portfolio management

9. Assume we can Avoid the “Sameness”, can Find the Upside, and Define Model Skill/Accuracy 1000 yrs stochastic 50 yrs history In-house stochastic crustal EQ catalogue Major available models however based on consensus hazard views: HERP, USGS, outsource your brain and accountability…

10. Consider 2+ sets of model results Model of choice for any territory or peril Average: Include two or more sets and divide event likelihood by number of sets Event match and complement: adjust activity rates Alter individual events, match, complement, and adjust activity rates Hazard Vulnerability Risk assessment 9

11. … what about: High chance that “Average” does not explain the next year “History” does not explain future “Consensus” is unlikely to explain the “common” outlier, Basins are not independent, and There might or might not be trends/regimes etc. …black swans?

12. Regimes & Dynamic Allocation of Capital ChangingRegimes NOAA Hurdat reanalysis: Storms in a box since 1851

13. The Skill of Forecasting,Cutting Through to Science

14. Towards: Ensemble set including wide range of short-term and long-term results allowing decision making skewed to company strategy and risk tolerance Peter Taylor, 2009

15. Beyond Expected Loss: Pricing the known known, unknown known… Peter Taylor, 2009

16. Beyond Expected Loss: Pricing the known known, unknown known… Peter Taylor’s Rumsfeld Quadrants Peter Taylor, 2009

17. Beyond Expected Loss: Pricing the known known, unknown known… It is as bad to over-estimate risk as it is to under-estimate it as both involve a cost… (D. Apgar, 2006). Loading is actually not N.N. Taleb’s idea! …Peter is not an UW… by the time we reach the unknown unknowns the deal is gone for us!

18. Willis Research Network at the End of 2009 Uncertainty, clustering, statistical modelling Catastrophe risk financing / public policy Environmental modelling, GIS, Remote Sensing ERM, operational risk and financial modelling Planning policy, vulnerability Hydrology, spatial statistics Urban flooding, meteorology Risk assessment, seismic risks, earth observation Storm surge, sea level rise Flood modelling and data Vulnerability, seismic risk, remote sensing Geological risks, groundwater flooding Asia-Pacific geohazards Climate risks and modelling Flood hydraulics, high performance computation, expert elicitation Climate risks, modelling Seismic risk, risk appetite Climate risks, hail risk, vulnerability, seismic risk Flooding, pollution Financial modelling, cost of capital Visualisation, informatics, risk communication Vulnerability, infrastructure Demand surge, vulnerability Climate and extreme weather, modelling Climate modelling, extreme weather Remote sensing, satellite data Climate drivers of extreme events, uncertainty Climate risks Climate risks, flooding Tsunami Geospatial data / systems 17

19. History of WRN 2nd Int. Climate Risks liaison group Official launch Int. Geospatial liaison group 2007 2008 2009 2010 1st annual Global Clients Meeting Bermudan reinsurers meeting 2nd annual Global Clients Meeting Bermudan reinsurers meeting European reinsurers meeting 3rd annual Global Clients Meeting Bermudan reinsurers meeting European reinsurers meeting 2010: Beijing Normal University Bogazici University GFDL Newcastle Oklahoma UNAM Universidad de Los Andes UWI Wharton, U Penn CEE flood v2.0 GCM TC track Hybrid Quake V1.0 (Tunisia) Demand surge methodology Cat Indices (e.g. WHI) ETC Clustering CEE Flood v1.0 Members per annum 18

20. WRN Challenges and Opportunities • Extremes:How much is random, what can be learned? • The Next Year; How relevant is the long-term average? • Actualistic Principle: Is history sufficient to predict future losses? • Nutshell numbers: Do we “Make everything as simple as possible, but not simpler” (Albert Einstein)? • Change: How do we cope/create opportunities with change?

21. Key Research 2010 Overarching research projects Demand Surge–Colorado University Business Interruptionand infrastructural risk - Kyoto University Risk & Uncertainty Visualisation –City University Extreme Value Statistics and Uncertainty–Exeter University Exposure, Post Event Calibration & Remote Sensing–Cambridge University Urban & Megacity Risk– All members High Performance Computation– All members Operational Risk, Cost of Capital and Public-Private Risk Transfer – ETH, Swansea, Wharton Flagship research projects • Hybrid loss model for seismic risks – first of its class: Tunisia • Imperial College, ROSE School Pavia, Cambridge University, Kyoto University, Colorado University • Extreme weather hazard modelling from GCMs: • Frequency, Severity, & Change • Walker Institute / Reading University, NCAR Colorado, National University Singapore, Systems Engineering Australia, University of Exeter • Regional flood risk: Central and Eastern European Flood • Bologna University, Exeter University, Fluvius Consulting (Vienna), Bristol University, Durham University, Princeton University , Newcastle 20 20

22. Managing Extremes & Insurance Decision Making • Global and conceptual • Global and operational • Regional & Local • Inform Existing Models • Create Additional Models where Model Penetration is insufficient • Solvency Margin, Capital Cost, & Rating • Decision Making Under Uncertainty • Alteration & Change, the current vs. future Underwriting Process Partnering with the world’s most influential decision makers Sharing best practice and key research outputs to redefine sustainability and shape future development policy Using knowledge of extremes and climate modelling technology to prepare for environmental change and protect essential resources Role of Re-insurance on Sustainability and Managing Extremes

23. Climate ChangeClimateWise, WRN • Building  public understanding on the importance of Climate Change, and ways to communicate risks and uncertainty in a more balanced way. • Measures for the insurance industry to better support public policy and regulation, e.g. through education at a individual (constituent) level. • How to deal with the non-availability of local level data/projections, that are needed for an effective response of the industry? • The role of insurance in adaptation, particularly the challenges of risk-based pricing and affordability. • What happens if global mean temperature exceed 2°C? • Decision making under deep uncertainty • Past not capable of predicting the future

24. Conclusion Our Future is related to multi-modelling und uncertainty subject to risk tolerance/culture of individual companies Related Challenges include: Individual model results with respect to range of possibilities? What is the “best ensemble” for which company? How do we make decisions/change our process under deep uncertainty? 23