ERM Symposium 2009 Property / Casualty Aspects of ERM Chris Suchar, FCAS, MAAA

Presentation Outline Property/Casualty a/k/a NonLife – How The Products Differ Risk Inventory ERM Approaches – Methodology, Metrics and Other Considerations

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

Risk Inventory

ERM Approaches – Methodology, Metrics and Other Considerations

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

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

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)

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)

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

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

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

Historical losses are normalized per unit of exposure – they typically exhibit:

Volatility

Trends

Cycles

Simple Example (cont’d) 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

Pricing/Underwriting Prices are based on analysis of claim experience from prior periods  subject to same parameter uncertainties Market prices exhibit cyclical behavior

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

Market prices exhibit cyclical behavior

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

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

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

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