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ERM Symposium 2009Property / Casualty Aspects of

ERMChris Suchar, FCAS, MAAA

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Presentation Outline

• Property/Casualty a/k/a NonLife – How The Products Differ

• Risk Inventory• ERM Approaches – Methodology, Metrics and

Other Considerations

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Characteristics of P/C Products

• Typically one-year (or shorter) policies, non-renewable at insurer’s discretion (sometimes restricted by regulation)

• Losses triggered by physical damage events and/or legal liabilities• Typically no policyholder options or behavior to consider, other than

renewal• No direct linkage to capital markets

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Risk Inventory for P/C Insurance

• Volatility of losses (frequency and severity)– Process variance– Parameter variance– Correlations

• Pricing/underwriting errors• Pricing/underwriting cycles• Reserve volatility and estimation errors

…. which can feed into pricing errors• Investment and ALM risks

– Historically considered secondary, if at all, by most insurers– Recent events have changed that mind-set– P/C liabilities can have long payment tails

• Reinsurance counterparties (unable or unwilling to pay)

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Loss Volatility

• Frequency and severity can both be highly uncertain in the sense of pure process variance

• Parameter uncertainty can be substantial:– Means change over time, due to

• Economic forces– Broad– In sectors, e.g., healthcare costs, construction costs

• Trends in legal system• Risk management, e.g. anti-lock brakes

– Tails of distributions difficult to estimate due to limited data – extrapolation of tails is often required

– Claim settlement lag often means tail estimates based on data from several years prior

• Correlation, especially in tails, also subject to parameter uncertainty

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Premium or Other

Exposure Measure

Ultimate Loss

Ultimate Loss Ratio

_____________ _____________ _____________

2004 1,050 916 0.872005 1,147 927 0.812006 1,228 879 0.722007 1,256 941 0.752008 1,257 1,040 0.83

2009 mean ??? ??? ???2009 vol ??? ??? ???

Simple Example of Uncertainties

Loss exposure (new and renewal business

volume) must be forecast

Historical losses are normalized per unit of

exposure – they typically exhibit:

• Volatility• Trends• Cycles

…..All of this often against a backdrop of data quality issues ranging from inaccuracy to heterogeneity

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Simple Example (cont’d)

Premium or Other

Exposure Measure

Paid In Year+0

Paid In Year+1

Paid In Year+2

Paid In Year+3

Total Paid to Date

Paid to Date Loss Ratio

Estimated Ultimate

Loss

Estimated Ultimate

Loss Ratio_____________ _____________ _____________ _____________ _____________ _____________ _____________ ________________ ______________

2004 1,050 251 255 254 157 916 0.87 916 0.872005 1,147 282 231 260 155 927 0.81 927 0.812006 1,228 277 232 223 ? 732 0.60 879 0.722007 1,256 230 260 ? ?? 489 0.39 941 0.752008 1,257 286 ? ?? ??? 286 0.23 1,040 0.83

2009 mean ??? ??? ???2009 vol ??? ??? ???

But wait, it gets worse…. the most recent (and therefore relevant) years in projecting

the future have not fully “matured” themselves – they have only been partly

observed, requiring estimation of the yet-to-emerge portion of the ultimate losses

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Pricing/Underwriting

• Prices are based on analysis of claim experience from prior periods subject to same parameter uncertainties

• Market prices exhibit cyclical behavior

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ERM Approaches

• Risk identification– Underwriting side

– Counterparties

– Economic risks

• Economic capital modeling– Monte Carlo simulation common, may need large number of scenarios

to get credible tail estimates

– Focus is projecting balance sheet X years in future

• X=1 common until recently

– Investment/economic risks treated simplistically in past, but catching up quickly to life side now

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ERM Approaches (cont’d)

• Metrics– Solvency at end of 1 year– Probability of rating action– Risk attribution– EVA– MCEV – rationale not as clear as for life/annuity products

• Other Considerations– Non-core operations– Group risk