Use of cat multi models for the insurance industry
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Use of Cat-Multi-Models for the Insurance Industry. Gero Michel. Conflicting Objectives: Commercial strategy: based on generating value short (to medium) term Inter-annual Variability: Many opportunities might not be profitable for one year

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Use of cat multi models for the insurance industry

Use of Cat-Multi-Models for the Insurance Industry

Gero Michel


Use of cat multi models for the insurance industry

  • 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?


Use of cat multi models for the insurance industry

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


Use of cat multi models for the insurance industry

  • Risk Tolerance:

  • Four types of companies:

  • Risk Averse

  • Risk Taking

  • Analytical/Managing

  • Pragmatists


Use of cat multi models for the insurance industry

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)


Use of cat multi models for the insurance industry

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


Catastrophe models defined el for almost any stretch peril territory

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”,


Use of cat multi models for the insurance industry

  • 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

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…


Consider 2 sets of model results

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


Use of cat multi models for the insurance industry

… 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?


Regimes dynamic allocation of capital

Regimes & Dynamic Allocation of Capital

ChangingRegimes

NOAA Hurdat reanalysis: Storms in a box since 1851


The skill of forecasting cutting through to science

The Skill of Forecasting,Cutting Through to Science


Use of cat multi models for the insurance industry

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


Beyond expected loss pricing the known known unknown known

Beyond Expected Loss: Pricing the known known, unknown known…

Peter Taylor, 2009


Beyond expected loss pricing the known known unknown known1

Beyond Expected Loss: Pricing the known known, unknown known…

Peter Taylor’s Rumsfeld Quadrants

Peter Taylor, 2009


Beyond expected loss pricing the known known unknown known2

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!


Willis research network at the end of 2009

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

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History of wrn

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


Wrn challenges and opportunities

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?


Key research 2010

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

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Managing extremes insurance decision making

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


Climate change climatewise wrn

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


Conclusion

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?

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