Wednesday 3 October 2001 John P Ryan Financial Condition Reporting Practical Aspects GIRO / CAS...
Transcript of Wednesday 3 October 2001 John P Ryan Financial Condition Reporting Practical Aspects GIRO / CAS...
Wednesday 3 October 2001
John P Ryan
Financial Condition Reporting Practical Aspects
GIRO / CAS Convention 2001
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Financial Condition Reporting - Practical Aspects
The FSA’s view
Assessment of individual risk
Modelling operational risk
Importance of tail dependency
Relevance of risk measures
Overlaying hard to quantify risks with a DFA model
Use of insurance to reduce capital requirements
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Institute of Actuaries paper on FCA
Provides a framework for evaluating a company’s financial position in relation to the risk it covers.
Concentrates on non-life insurance but covers the principles for all companies.
It covers both readily quantifiable risks and those not so readily quantifiable e.g. management succession risks.
The Profession’s response to the FSA proposal.
FSA will apply to all financial Institutions.
Corley Report also calls for FCR reports for Life Co’s
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Identify
Administer Control
Finance
Risk Management Circle
Effective control requires quantification
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Individual Risk Assessment.
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Methods of Modelling Risk
Financial Risk - investment models
Financial Liabilities - actuarial models
All Other - as operational risk
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Insurance Company Risks
R etrospectiveB alance sheet R isks
ProspectiveB usiness R isks
Financial
O perational External
N on Financial
R isks
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Financial Risks
Asset R isks Liability R isks
R etrospectiveB alance sheet R isks
Financial
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Asset Risks
Value, Bad debt
Modelling volatility
Market value
Reinsurance
Market
?Valuation
Concentration
ACTUARIAL ASSESSMENTRISK
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Liability Risks
Discounting
Unexpired Risks
Unearned Premiums
Outstanding claims / IBNR
ACTUARIAL ASSESSMENTRISK
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Liability Risks
Mismatch
ACTUARIAL ASSESSMENTRISK
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Financial Risks
U nderw riting R isks Exposure R isks B usiness R isks Financing R isksC apital / D ebt
PropectiveB usiness R isks
Financial
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Underwriting Risks
Impact on pricing assumptions
But varies by class
Concentration
?Acceptance
?
Growth / new classes
Pricing
ACTUARIAL ASSESSMENTRISK
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Exposure Risks
?Policyholders’ Reasonable Expectations
Claims frequency / severity
Reinsurance
PMLs
ACTUARIAL ASSESSMENTRISK
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Business Risks
Investment Strategy
?Mergers & Acquisitions
Expenses
ACTUARIAL ASSESSMENTRISK
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Financing Risks
Gearing
Debt interest / repayment
Dividend commitments
ACTUARIAL ASSESSMENTRISK
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Insurance Company Risks
R etrospectiveB alance sheet R isks
ProspectiveB usiness R isks
Financial
O perational External
N on F inancial
R isks
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Operational Risks
But might come across evidence
But might help with system requirements
Could help assess some procedures
But might come across evidence
XManagement
XTechnology
?Administrative
XFraud
ACTUARIAL ASSESSMENTRISK
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Operational Risks
DFA / Market Analysis
XReputation
?Data quality / availability
Planning
ACTUARIAL ASSESSMENTRISK
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External Risks
Possible future. Some known changes
XConfiscation / Nationalisation
XPolitical
Taxation
?Social
?
Legal / Legislative
ACTUARIAL ASSESSMENTRISK
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External Risks
XRegulatory
Group structure
?Dependency
ACTUARIAL ASSESSMENTRISK
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Operational Risk
ASSESSMENT OF OPERATIONAL RISK
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Management and Business Risk
Some can be modelled using econometric or causal modelling techniques
Some are really risks for shareholders rather than capital issues
Stress testing can be a useful quantification technique
Insurance often cannot be used for this type of risk
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Quantification of Operational Risk
Operational Risk
Delphi Techniques
Produce a Model
Quantify Risk
CorroborateResults
Collect Data
Industry Specific
Model
Quantify
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Development of loss curves
BudgetedLoss
Amount of Loss
ExpectedLevelof Loss
ProbabilityofLoss
Based on data
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Quantification of Operational Risk
It is more complex than pricing conventional insurance risk
The risks are more under control of the institution than many insured perils
Changes in practice can have a material impact
Organisations do not like to admit to Operational Risk losses
Some are not readily amenable to statistical analysis e.g. management succession risk
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Scenarios
Distributions may not be the best approach to evaluating certain types of operational risk
Test the survival of the organisation to adverse scenarios
Especially suitable for “people risks” e.g. succession planning
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Data based approach
Not many databases around
Not all losses are disclosed
Controls and mode of operation may render some data to be inappropriate
Low frequency / high severity risk requires a different approach
Some “operational risks” are budgeting items
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InternalInterviews
Example Output from a Large Loss Study
50% 60% 70% 80% 90%
Reliability of Loss Estimates
OwnClaims
Cla
ims S
ize
(US
$ b
illion
s)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
ExternalInterviews
ExternalResearch
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Development of loss curves
BudgetedLoss
Amount of Loss
ExpectedLevelof Loss
ProbabilityofLoss
Based on dataIncluding interviews
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Data for loss curve
Loss Limit
100
80
60
40
20
Amount ofLoss in Data
80
80
80
70
50
Amount of Losses in Data & Interviews
105
100
90
75
50
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Questions
The difficulty is the need to estimate the right tail in a skew distribution
How good is the left of the curve at predicting the right tail
Use of Bayesian statistics or credibility theory
What distributions fit the data
What techniques are best at supplementing the data for “missing large claims”
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Conclusions - Data based methods
Data based methods are the traditional actuarial technique for insurance claims, they are intuitively acceptable
There are major deficiencies in using data based methods for operational risk not present in insurance data
The major problem is non-reporting of large claims
Useful check on the reality of other methods
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What are the other methods?
Delphi techniques
Decision trees and casual modelling
? Fuzzy Logic
? Others
? Use data bases for left side and other techniques for right side
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Delphi Technique
Key Drivers of Business Unit
What are the risks to each Business Unit
What are the likely frequency
If loss occurs what is the likely cost
Fit curves following interview technique
Model uncertainty
Combine all results
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Core Operational Risk Analysis
Framework involves Business Process and Resource / Risk Classes
ResourceClasses
PhysicalAssets Technology People
Relationship(Liability)
OtherExternal
BusinessProcess
TransactionalProcess
BusinessManagement
Reputation
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Risk profiles are linked to financial measures using a financial value tree approach
Fixed AssetsDistribution
Costs
Price
Taxes
Free Cash Flow
Op. Cash Flow Investment
Gross Margin
Revenues
Volume
Working Capital
Event Risk Financial Risk Distribution Channel Business Risk
Probability Distribution of Economic Loss Due to Fire Risk
0%2%4%6%8%
10%
16.8
2
42.8
3
68.8
3
94.8
4
120.
84
146.
84
172.
85
198.
85
224.
86
250.
86
Pro
babi
lity
Probability Distribution of Price Volatility
0%1%2%3%4%5%6%7%8%9%
10%
Probability Distribution of Lead Time to Market Due to Strike
0%
2%
4%
6%
8%
10%
12%
Probability Distribution of Market Share Lost Due to New Entrant
0%2%
4%6%
8%10%
0.20
9
0.24
3
0.27
7
0.31
1
0.34
5
0.37
9
0.41
3
0.44
7
0.48
1
0.51
5
Pro
babi
lity
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Importance of the risk measure
Var implicitly assumes “elliptic risks”
Operational risk does not satisfy this condition
Market Risk needs to be frequently updated hence the importance of Var
Operational Risk does not change rapidly
Hence “equivalent Var” will not change rapidly
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Adding the efficient frontiers will overstate the costs for a given risk as no adjustment is made for diversification credits
This at a minimum changes the choices or risk loadings even if all strategies are ranked in the same way
Risk
Cost
AddingEach
Separately
Evaluating risks altogether
Consolidatedlargely independentrisks
Consolidated with manytail dependent risks
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Risk Measures
Var works well for symmetrical risks
ECOR is better for skew risks such as most insurance risks
A coherent measure needs to be used across the group as a whole
Beware of tail dependency
Other constraints are also needed such as a requirement to maintain a credit rating
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ECOR reflects both the probability and the severity of ruin
ECOR is the present value of expected deficits in excess of economic capital
ECOR is derived as the probability of a loss times the severity of the loss In today’s dollars Sum of all loss events Reflects solvency risk tolerance measure and assigned
capital
The “ECOR ratio” is the ECOR divided by the present value of expected customer payments
ECOR is a better solvency risk measure than probability of ruin because it reflects the cost of ruin, not simply the
likelihood of event
ECOR is a better solvency risk measure than probability of ruin because it reflects the cost of ruin, not simply the
likelihood of event
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Why Does This Matter?
CombinedOperational Risk
InvestmentRisk
Var
ECOR
The RBC’s are very different for different approaches
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Coherent Risk Measures
To be coherent a risk measure (p) must satisfy four
conditions:
(i) Translation Invariance p(x + .r) = p(x) -
(ii) Sub additivity p(x1+ x2) p(x1) + p(x2)
(iii) Positive homogeneity for o p(x)= p(x)
(iv) Monotoniaty If x y p(Y) p(x)
Var fails the sub additivity property
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Insurance to cover Operational Risk
This is a non-trivial subject.
Basel has many doubts.
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Coverage Gaps
If complete cover is not available then capital will need to be held against remaining risk
Insurance should mitigate operational risk cost and so should be allowable
Operational Risk models would need to be run with and without insurance
Contracts with material exclusions may not mitigate overall capital requirements much
All Risks Cover is preferable
Much operational risk violates an underwriting rule that the insured should not be able to manipulate his loss experience
Some risks may not be insurable e.g. management succession risk
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Claims Disputes
Some financial impact as a dispute creates coverage gap
Change insurance practice of conducting investigations at point of claim to investigating at point of sale
Financial Enhancement Ratings (FER)
Different in conditions (DIC) coverage