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Financial Condition Reporting
Practical Aspects
GIRO / CAS Convention 2001
John P Ryan
Wednesday 3 October 2001
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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Risk Management Circle
Identify
Administer
Control
Finance
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
Risks
Financial
Retrospective
Balance sheet Risks
Prospective
Business Risks
Non Financial
Operational
External
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Financial Risks
Financial
Retrospective
Balance sheet Risks
Asset Risks
Liability Risks
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Asset Risks
RISK
ACTUARIAL ASSESSMENT
Concentration

Valuation
?
Market
 Modelling volatility
Reinsurance
 Value, Bad debt
Market value
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Liability Risks
RISK
ACTUARIAL ASSESSMENT
Outstanding claims / IBNR

Unearned Premiums

Unexpired Risks

Discounting

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Liability Risks
RISK
Mismatch
ACTUARIAL ASSESSMENT

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Financial Risks
Financial
Propective
Business Risks
Underwriting Risks
Exposure Risks
Business Risks
Financing Risks
Capital / Debt
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Underwriting Risks
RISK
ACTUARIAL ASSESSMENT
Pricing
 But varies by class
Growth / new classes
 Impact on pricing
? assumptions
Acceptance
?
Concentration

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Exposure Risks
RISK
ACTUARIAL ASSESSMENT
PMLs

Reinsurance

Claims frequency / severity

Policyholders’ Reasonable
Expectations
?
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Business Risks
RISK
ACTUARIAL ASSESSMENT
Expenses

Mergers & Acquisitions
?
Investment Strategy

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Financing Risks
RISK
ACTUARIAL ASSESSMENT
Dividend commitments

Debt interest / repayment

Gearing

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Insurance Company Risks
Risks
Financial
Retrospective
Balance sheet Risks
Prospective
Business Risks
Non Financial
Operational
External
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Operational Risks
RISK
ACTUARIAL ASSESSMENT
Fraud
X
But might come across
evidence
Administrative
?
Could help assess some
procedures
Technology
X
But might help with
system requirements
Management
X
But might come across
evidence
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Operational Risks
RISK
ACTUARIAL ASSESSMENT
Planning
 DFA / Market Analysis
Data quality / availability
?
Reputation
X
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External Risks
RISK
ACTUARIAL ASSESSMENT
Legal / Legislative
? Possible future. Some
 known changes
Social
?
Political
X
Taxation
Confiscation / Nationalisation
X
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External Risks
RISK
ACTUARIAL ASSESSMENT
Dependency
?
Group structure

Regulatory
X
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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
Collect Data
Delphi Techniques
Industry
Produce a Model
Specific
Model
Quantify Risk
Quantify
Corroborate
Results
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Development of loss curves
Probability
of
Loss
Expected
Level
of
Loss
Budgeted
Loss
Amount of Loss
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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Example Output from a Large Loss Study
Reliability of Loss Estimates
1
Own
Claims
0.9
Claims Size
(US$ billions)
0.8
External
Research

Internal
Interviews
External
Interviews
0.7
0.6

0.5



0.4
0.3

0.2


 
0.1
0.0
 
 
  





50% 60% 70% 80% 90%
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Development of loss curves
Probability
of
Loss
Expected
Level
of
Loss
Budgeted
Loss
Amount of Loss
Based on data
Including interviews
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Data for loss curve
Loss
Limit
Amount of
Loss in Data
Amount of Losses in
Data & Interviews
100
80
105
80
80
100
60
80
90
40
70
75
20
50
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
Physical
Assets
Technology
People
Relationship
(Liability)
Other
External
Resource
Classes
Business
Process
Transactional
Process
Business
Management
Reputation
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Risk profiles are linked to financial measures using a financial value
tree approach
Free Cash Flow
Investment
Op. Cash Flow
Gross Margin
Revenues
Distribution
Taxes
Working Capital
Costs
Volume
Price
Event Risk
Financial Risk
Distribution Channel
Business Risk
Probability Distribution of
Economic Loss Due to Fire Risk
Probability Distribution of
Market Share Lost Due to New Entrant
0.515
0.481
0.447
0.413
0.379
0.345
0.311
0.277
0.243
10%
8%
6%
4%
2%
0%
0.209
250.86
224.86
198.85
172.85
146.84
94.84
120.84
68.83
42.83
Probability
10%
8%
6%
4%
2%
0%
16.82
Probability
Fixed Assets
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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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Evaluating risks altogether
Adding
Each
Separately
Risk
Consolidated with many
tail dependent risks
Consolidated
largely independent
risks
Cost
 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
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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
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Why Does This Matter?
The RBC’s are very different for different approaches
Var
ECOR
Combined
Operational
Risk
Investment
Risk
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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
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