Considering Risk Appetite Using Quantitative Risk
Analysis
29 May 2012
Sydney
Andrew Kight
Why undertake risk analytics?
Median TCOIR Per $1,000 of Revenue ($)
Organisations that:
Use risk analytics
Do not use risk analytics
Difference
2009/10
5.52
7.59
-27.3%
2010/11
6.44
7.63
-15.6%
Source: Aon’s Australasian Risk Management Benchmarking Survey
ƒ Median TCOIR per $1,000 Revenue over the last two years is 21.4% less
for organisations that use risk analytics
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Outline
ƒ Risk Appetite Theory
ƒ Case Study – Insurable Risk
ƒ Further Considerations
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Definitions
ƒ There are various definitions of risk tolerance and risk appetite, however, we
define them as follows:
– Risk Tolerance: an organisation’s financial ability to retain additional risk. On a
strict interpretation of the definition it would describe the ability to absorb an
unbudgeted loss whilst continuing as a going concern.
– Risk Appetite: an organisation’s desire to retain risk and will depend on the
views of the Board and management (conservative or aggressive), as well as
being influenced by other stakeholders (banks, governments and shareholders /
owners).
ƒ Tolerance should be bigger than Appetite?!
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How to express Risk Appetite
ƒ This is not easy!
ƒ Car Insurance Example
Excess ($)
Premium ($)
Option 1
500
1,000
Option 2
1,000
800
Option 3
5,000
400
Option 4
No insurance
0
ƒ What is driving your decision?
– Risk vs Reward?
– Risk Appetite?
– Both?
ƒ How would you express your risk appetite?
ƒ Is it qualitative or quantitative?
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Aon’s Global ERM Survey
ƒ 43% of organisations new to ERM (Initial /
Lacking & Basic) still evaluate risk appetite
based on ‘gut feel’
ƒ 0% of Advanced ERM organisations
evaluate risk appetite on ‘gut feel’
ƒ 26% of survey participants do not have a
formalised risk appetite evaluation
methodology
ƒ 23% of survey respondents have had
success quantifying risk appetite / tolerance
“Origin Energy now takes a very quantitative approach to risks and expresses them in terms of the volatility they
introduce to key financial measures reflecting profitability, liquidity and our equity position.”
John Rodda, Origin Energy
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Aon’s Global ERM Survey
ƒ Organisations with mature ERM programs use
more sophisticated quantification techniques to
leverage risk-based information to support decision
making
ƒ 43% of Advanced ERM organisations utilise
stochastic / Monte Carlo simulation to understand
risk and measure the value of ERM
ƒ While most respondents with mature ERM
programs utilise qualitative tools to assess risk, the
use of sophisticated quantification tools including
value at risk, actuarial analysis and stochastic
modelling drop significantly
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How to express Risk Appetite
Risk Appetite Statements
ƒ Can be qualitative and quantitative
–
–
–
–
–
Financial measures (ie. profit, banking covenants etc.)
Based on likelihood and impact grid
Qualitative statements
Key indicators – risk, control and performance
Impact on credit ratings
ƒ Our job (and our challenge):
– Qualitative statements into quantitative amounts
– Translate technical / quantitative models for decision-makers
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Sensitivity Analysis
g
Sensitivity Analysis - Earnings Before Interest & Tax (EBIT)
$500m
$450m
$446.2m
$436.2m
$421.2m
$396.2m
$400m
Earnings Before Interest & Tax
EBIT cannot drop more than 25% below budget
$346.2m
$350m
$300m
$246.2m
$250m
$200m
$146.2m
$150m
$100m
$50m
$0m
2011
$10m
$25m
$50m
$100m
$200m
$300m
Unbudgeted Loss Scenario
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Risk Categories
ƒ The entire risk portfolio must
be taken into consideration
when making decisions on risk
appetite
ƒ How is the aggregate risk
appetite spread across
different risk categories?
ƒ Insurable risk: deemed to be
between 30% to 40% of a risk
portfolio
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Risk financing strategy
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Case Study
Details
ƒ Large Manufacturing Company
ƒ Significant growth in last five years
Scope
ƒ Determine the optimal insurable risk financing strategy
How are we to do this?
1.
2.
3.
Sensitivity Analysis -> Determine insurable risk appetite
Provide Options Within Appetite -> Test existing program structure is within risk
appetite and alternative options for consideration
Total Cost Comparison -> Calculate most cost-efficient option
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Sensitivity Analysis
g
Sensitivity Analysis - Interest Cover
14
12.6
12.1
12
11.4
Interest Cover (times)
10.3
Interest Cover cannot drop below 3.0
10
8.4
8
6
5.2
4
2.8
2
0
Budget
$10m
$25m
$50m
$100m
$200m
$300m
Self-Insured Loss Scenario
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Sensitivity Analysis
g
Sensitivity Analysis - Earnings Per Share
45
40
39.8
38.8
37.2
35
Earnings Per Share (cents)
EPS cannot be less than 20 cents per share
34.6
29.4
30
25
18.9
20
15
10
8.4
5
0
Budget
$10m
$25m
$50m
$100m
$200m
$300m
Self-Insured Loss Scenario
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Sensitivity Analysis
g
Sensitivity Analysis - Earnings Before Interest & Tax (EBIT)
$500m
$450m
$446.2m
$436.2m
$421.2m
$396.2m
$400m
Earnings Before Interest & Tax
EBIT cannot drop more than 25% below budget
$346.2m
$350m
$300m
$246.2m
$250m
$200m
$146.2m
$150m
$100m
$50m
$0m
Budget
$10m
$25m
$50m
$100m
$200m
$300m
Unbudgeted Loss Scenario
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Program Structure
Risk Class
Deductible ($)
Directors' and Officers' Liability
10,000
Professional Indemnity
25,000
9
9
8
8
8
500
500
1,000
8
8
8
Property
10,000,000
Liability
10,000,000
Motor
Minor Risk Classes
‐ Corporate Travel
‐ Personal Accident
‐ Marine Transit
‐ etc…
1,000
ƒ Which risk classes are relevant from a risk appetite perspective?
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Risk Appetite
ƒ
ƒ
ƒ
ƒ
Assume Group Risk Appetite is $100m
$40m is allocated to Insurable Risk
How can we use this?
Let’s test this against the existing program structure
Loss Experience
ƒ Property: Two large losses in last five years
ƒ Liability: One large loss in last five years
Deterministic Approach
Risk Class
Property
Liability
Total
Deductible
$10m
$10m
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Worst Case Number of Losses
2
2
Max Exposure
$20m
$20m
$40m
ƒ What conclusions can be
drawn from this?
ƒ What else needs to be
considered?
– Likelihood
– Correlation
16
Loss Analysis
Loss Frequency Analysis (Losses over $1m)
Property
Year
Exposure
Count
2008
2009
2010
2011
2012
2013
Total (2008 ‐ 2012)
Freq per 1,000 Exp
Forecast (2013)
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2,000
2,500
2,500
4,000
4,500
5,000
15,500
5,000
0
1
0
1
0
Exposure
2,000
2,500
2,500
4,000
4,500
5,000
Liability
% Dev
Dev Exp
100%
95%
90%
60%
20%
Count
2,000
2,375
2,250
2,400
900
0
0
1
0
0
2
0.129
9,925
1
0.101
0.65
5,000
0.50
17
Selected Distributions
Loss Band
Attritional
(Losses < $1m)
Property
mean
2,000,000
std dev
1,000,000
Liability
mean
3,000,000
std dev
500,000
Large Losses
(Losses $1m to $50m)
Freq
Freq
0.65
Severity
mean
std dev
lower
upper
Catastrophic Losses
(Losses > $50m)
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Lognormal
10,000,000
20,000,000
1,000,000
50,000,000
Not applicable
0.50
Severity
mean
std dev
lower
upper
Lognormal
15,000,000
16,000,000
1,000,000
50,000,000
Not applicable
18
Results
Loss Forecast
Retained Losses
Deductibles ($m)
Property
Liability
Annual Losses ($m)
Average
Std Dev
Unbudgeted Losses
10
10
12.6
8.0
Risk Appetite ($m)
Return Periods ($m)
1 in 4 year high
1 in 10 year high
1 in 20 year high
1 in 50 year high
1 in 100 year high
1 in 250 year high
0.0
8.0
40.0
16.6
24.2
27.7
33.6
36.5
41.6
4.0
11.5
15.1
21.0
23.9
28.9
* assumes that average losses are budgeted
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Results
Retained Losses
Current Option 1 Option 2 Option 3
Deductibles ($m)
Property
Liability
Annual Losses ($m)
Average
Std Dev
10
10
20
20
30
30
50
50
12.6
8.0
15.4
11.9
16.5
13.9
17.0
15.2
Risk Appetite ($m)
Return Periods ($m)
1 in 4 year high
1 in 10 year high
1 in 20 year high
1 in 50 year high
1 in 100 year high
1 in 250 year high
16.6
24.2
27.7
33.6
36.5
41.6
23.2
31.6
39.0
46.7
52.9
60.4
23.2
36.0
44.2
54.9
62.8
71.3
23.2
38.7
48.8
60.3
68.7
79.9
Probability: Unbudgeted Losses > Risk Appetite
Unbudgeted Losses
Current Option 1 Option 2 Option 3
0.0
8.0
0.0
11.9
0.0
13.9
0.0
15.2
40.0
40.0
40.0
40.0
4.0
11.5
15.1
21.0
23.9
28.9
7.8
16.3
23.6
31.3
37.5
45.1
6.8
19.6
27.7
38.5
46.3
54.9
6.2
21.7
31.8
43.3
51.7
62.9
0.04%
0.82%
1.81%
2.68%
* assumes that average losses are budgeted for each option
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Selected Distributions
Unbudgeted Risk vs Risk Appetite
70
60
Unbudgeted Risk ($m)
50
40
30
20
10
0
1 in 10 year high
1 in 20 year high
1 in 50 year high
1 in 100 year high
1 in 250 year high
Return Periods
Current
$10m
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Option 1
$20m
Option 2
$30m
Option 3
$50m
Risk Appetite
21
TCOIR Comparison
Current
Deductibles
Property
Liability
Base Premium ‐ Property
Base Premium ‐ Liability
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TCOIR Comparison
Option A
Option B
Option C
$10m
$10m
$20m
$10m
$10m
$20m
$20m
$20m
4,000,000
3,500,000
2,500,000
3,500,000
4,000,000
1,500,000
2,500,000
1,500,000
22
TCOIR Comparison
Current
Deductibles
Property
Liability
TCOIR Comparison
Option A
Option B
Option C
$10m
$10m
$20m
$10m
$10m
$20m
$20m
$20m
Base Premium ‐ Property
Base Premium ‐ Liability
4,000,000
3,500,000
2,500,000
3,500,000
4,000,000
1,500,000
2,500,000
1,500,000
Total Base Premium
Statutory Charges
Average Retained Losses
7,500,000
1,480,000
12,655,871
6,000,000
1,030,000
13,789,305
5,500,000
1,320,000
14,257,066
4,000,000
870,000
15,390,499
Average Total Cost of Insurable Risk
21,635,871
20,819,305
21,077,066
20,260,499
0
816,567
558,806
1,375,372
Average Saving From Current
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Within Risk Appetite?
Assume TCOIR Budget of $20m:
Unbudgeted Risk ($m)
1 in 10 year high
1 in 20 year high
1 in 50 year high
1 in 100 year high
1 in 250 year high
Current
Option A
Option B
Option C
13.2
16.7
22.8
25.8
30.9
14.1
20.6
27.4
32.2
38.1
15.5
21.8
29.8
34.6
41.3
16.5
24.3
31.9
37.7
46.2
ƒ Option C is the optimal program structure
– It is cheapest option from a ‘total cost’ perspective and is still within the
organisation’s insurable risk appetite
ƒ What if we don’t need $40m?
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Other Uses
ƒ Models exist for other risk
categories, so this process can
be easily replicated for other
risk categories.
ƒ Risk Appetite sets the strategy
for how risk is financed across
the organisation and within
each risk category
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A Broader View of a Risk Portfolio
ƒ Let’s look a things across the whole organisation (or across a project)
ƒ Develop a risk register:
– Inherent risk (Gross Risk): before controls
– Controls: Risk mitigation (ie. contracts, RM procedures, insurance etc.)
– Residual risk (Net Risk): after controls
– Likelihood and Impact ratings – using ratings scales (ie. 1 to 5)
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Risk Matrix
Risk Score
Likelihood Rating
1
Certain / Will occur
For Example
An event you can expect to happen
(Once per year or more)
2
Likely to occur
An event that can be anticipated to happen and
this area or a similar organisation have
experienced such an event
(1 in 3 year event)
3
Possible / Could occur or have
heard of it happening
An event that can be envisaged but has not
occurred in this area or in this organisation
(1 in 10 year event)
4
Unlikely to occur
An event that can be envisaged but hasn’t
occurred in the company history (e.g. requires a
combination of two or more events to occur)
(1 in 30 year event)
5
Rare / Practically impossible
An event that can be conceived but is
considered to be very difficult to realise (e.g.
requires a combination of several events to
occur)
(1 in 100 year event)
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A Simplistic Example
Risk
Risk 1
Risk 2
Risk 3
Risk 4
Risk 5
Risk 6
Risk 7
Risk 8
Risk 9
Risk 10
Risk 11
Risk 12
Risk 13
Risk 14
Risk 15
Risk 16
Risk 17
Risk 18
Risk 19
Risk 20
Residual Risk Rating
Likelihood Impact
2
5
1
5
3
4
2
4
1
4
3
3
3
3
2
3
4
2
3
2
3
2
2
2
2
2
2
2
1
2
4
1
3
1
3
1
2
1
2
1
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Likelihood
1 in 30
1 in 100
1 in 10
1 in 30
1 in 100
1 in 10
1 in 10
1 in 30
1 in 3
1 in 10
1 in 10
1 in 30
1 in 30
1 in 30
1 in 100
1 in 3
1 in 10
1 in 10
1 in 30
1 in 30
Impact
>$25m
>$25m
$10m ‐ $25m
$10m ‐ $25m
$10m ‐ $25m
$2.5m‐ $10m
$2.5m‐ $10m
$2.5m‐ $10m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
<$500k
<$500k
<$500k
<$500k
<$500k
Validated Impact
$60m ‐ $80m
$50m ‐ $150m
$10m ‐ $25m
$10m ‐ $25m
$10m ‐ $25m
$2.5m‐ $10m
$2.5m‐ $10m
$2.5m‐ $10m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
$500k ‐ $2.5m
<$500k
<$500k
<$500k
<$500k
<$500k
Likelihood
RiskBinomial
Impact
RiskPert
28
Sample Distribution of Unbudgeted Risk
Unbudgeted Risk vs Risk Appetite
90%
Unbudgeted Risk exceeds Risk Appetite 0.6% of the time
80%
70%
Percentile
60%
50%
40%
30%
20%
10%
0%
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
Amount($'000)
Unbudgeted Risk
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Risk Appetite
29
Further Considerations and Improvements
Sensitivity Analysis - Earnings Before Interest & Tax (EBIT)
$500m
$450m
EBIT cannot drop more than 25% below budget
$446.2m
$425.8m
$417.0m
Earnings Before Interest & Tax
$400m
$370.5m
$355.8m
$334.4m
$350m
$300m
$250m
$200m
$150m
$100m
$50m
$0m
Budget
1 in 10
year high
1 in 20
year high
1 in 50
year high
1 in 100
year high
1 in 250
year high
Unbudgeted Loss Scenario
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Further Considerations and Improvements
ƒ Sensitivity Analysis of Model Inputs
ƒ Detailed Validation of Likelihood and Severity of major risks
ƒ Correlation
ƒ Upside risk?
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Contact Information
Andrew Kight
Aon Global Risk Consulting
+61 3 9211 3205
andrew.kight@aon.com
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