Use of Cat-Multi-Models for the Insurance IndustryGero Michel

2

Conflicting Objectives: Commercial strategy: based on generating value short (to medium) termInter-annual Variability: Many opportunities might not be profitable for one yearDiversification: The insurance world is too small to diversify cat risk away History based: might not be sufficient to forecast the futureShort-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

1. Only the Analysts and Pragmatists might want to use models (top-down or bottom up) but

2. 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

5 to 5 .5 5 .5 to 6 6 to 6 .5 6 .5 to 7 7 .5 to 8 8 to 8 .3

“Trials of Stochastic event sets” limited by “ knowledge, computer power, and imagination”,

300 yrs GCM10,000 yrs statistical

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

Assume we can Avoid the “Sameness”, can Find the Upside, and Define Model Skill/Accuracy

9

5 .5 to 7 7 to 7 .5 7 .5 to 8 8 to 9

In-house stochastic crustal EQ catalogueMajor available models however based on consensus hazard views: HERP, USGS, outsource your brain and accountability…

1000 yrs stochastic50 yrs history

1010

Consider 2+ sets of model

results1. Model of choice for any

territory or peril2. Average: Include two or

more sets and divide event likelihood by number of sets

3. Event match and complement: adjust activity rates

4. Alter individual events, match, complement, and adjust activity rates

Risk assessmentHazard Vulnerability

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, andThere might or might not be trends/regimes etc.…black swans?

12

Regimes & Dynamic Allocation of Capital

NOAA Hurdat reanalysis: Storms in a box since 1851

Changing Regimes

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, 2009

Peter Taylor’s Rumsfeld Quadrants

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!

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!

1818

Willis Research Network at the End of 2009

Environmental modelling, GIS, Remote Sensing

Planning policy, vulnerability

Hydrology, spatial statistics

Vulnerability, seismic risk, remote sensing

Geological risks, groundwater flooding

Flooding, pollution

Visualisation, informatics, risk communication

Demand surge, vulnerability

Flood hydraulics, high performance computation, expert elicitation

Climate drivers of extreme events, uncertainty

ERM, operational risk and financial modelling

Flood modelling and data

Risk assessment, seismic risks, earth observation

Climate risks, hail risk, vulnerability, seismic risk

Seismic risk, risk appetite

Climate and extreme weather, modelling

Remote sensing, satellite data

Climate modelling, extreme weather

Climate risks, flooding

Geospatial data / systems

Catastrophe risk financing / public policy

Asia-Pacific geohazards

Urban flooding, meteorology

Storm surge, sea level rise

Climate risks and modelling

Climate risks, modelling

Financial modelling, cost of capital

Climate risks

Vulnerability, infrastructure

Tsunami

Uncertainty, clustering, statistical modelling

1919

History of WRN

2007 2008 20091st annual Global Clients Meeting

3rd annual Global Clients Meeting

Official launch

ETC Clustering

CEE Flood v1.0

Demand surge methodology

2nd annual Global Clients Meeting

2010

2010:Beijing Normal UniversityBogazici UniversityGFDLNewcastleOklahomaUNAMUniversidad de Los AndesUWIWharton, U Penn

CatIndices(e.g. WHI)

CEE flood v2.0

GCM TC track

Hybrid QuakeV1.0 (Tunisia)

Int. Geospatial liaison group

European reinsurers meeting

Bermudan reinsurers meeting

Bermudan reinsurers meeting

Bermudan reinsurers meeting

European reinsurers meeting

2nd Int. Climate Risks liaison group

0

5

10

15

20

25

30

35

40

45

2006 2007 2008 2009 2010

Pa

rtn

er

Ins

t.

Members per annum

20

WRN Challenges and Opportunities1.Extremes: How much is random, what can be learned?

2.The Next Year; How relevant is the long-term average?

3.Actualistic Principle: Is history sufficient to predict future losses?

4.Nutshell numbers: Do we “Make everything as simple as possible, but not simpler” (Albert Einstein)?

5.Change: How do we cope/create opportunities with change?

2121

Key Research 2010

21

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

Overarching research projects

Demand Surge –Colorado University

Business Interruption and 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

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

2424

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?