Improving Risk Management
Unravelling the complexity of risk
Neil Cantle
Joshua Corrigan
Institute of Actuaries of Australia
ERM Seminar
20 September 2011
Contents
1. Complex Systems Framework for Risk Analysis
2. A New Toolset for Complex Risk Analysis
3. Australian Case Study
4. UK Actuarial Profession Risk Appetite Research
5. Summary
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Complex Systems Framework for
Risk Analysis
Section 1
Starting Point
 Previous study leads us to the view that:
– Risk tools need to embrace
• Holism
• Non-linearity / complexity
• Human bias
• Adaptation / evolution
– Risk can be viewed as the unintended emergent property of a
complex adaptive system
– Risks are a process and even complex risks can be spotted early
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© 2011 Milliman
Systems Thinking
 Systems thinking is both a:
– Worldview that:
• Problems cannot be addressed by reduction of the system
• System behaviour is about interactions and relationships
• Emergent behaviour is a result of those interactions
– Process or methodology to:
• Understand complex system behaviour
• See both the “forest and the trees”
• Identify possible solutions and system learning
• Utilise complexity science techniques for risk analysis
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A New Perspective on Risk
Systems
dynamics
Cladistics
Graph
theory
Behavioural
science
Complex
systems
Bayesian
networks
Information
Theory
Psychology
There are a lot of sciences which have
insights to offer in relation to the study
of complex adaptive systems...
...putting them together makes many
difficult risk management tasks easier,
and even possible
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Cognitive
mapping
Understanding a Crisis
Symptoms
Causes
Sense-making
Understanding
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Complex Adaptive Systems
 Basic properties:
–
–
–
–
–
–
–
–
Has a purpose
Emergence – the whole has properties not held by sub components
Self Organisation – structure and hierarchy but few leverage points
Interacting feedback loops – causing highly non-linear behaviour
Counter-intuitive and non-intended consequences
Has tipping point or critical complexity limit before collapse
Evolves and history is important
Cause and symptom separated in time and space
Risk is the unintended emergent property of a company
(which is a complex adaptive system)
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A Systems View Of Risk
 Holism before reductionism (think “outcomes”)
 Embrace human cognitive biases (and adjust inputs)
 Admit non-linearity
 Cope with adaptation (avoid static reporting/analyses)
 Simple behaviours and feedback can produce complex outcomes
 Risk is an evolutionary process not a point in time event
Complexity-based techniques reveal buried truths and
make the management of risk more intuitive
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A New Toolset for Complex Risk
Analysis
Section 2
Cognitive Mapping - It’s all in your head!
People form complex models in their
head of what they see/think. As your
experts describe those models it is
possible to use cognitive mapping
techniques to reconstruct the highly
complex risk profiles of real business
in a robust, repeatable way.
You can evidence areas where
narrative is too brief or where there
are conflicting views.
It is a natural way for experts to
engage but helps them combine their
thoughts with others and identify the
really important facts.
Source: Milliman
Key Nodes
Key Drivers
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Gaps
Case Study
 UK Life Assurer had a series of
operational risk scenarios which were
monitored regularly and had been
modelled as loss-distributions
Cognitive map of
scenario description
 Lack of real engagement between
capital modellers and business as the
model was a bit “abstract”
 Scenarios were discussed with business
experts who described the features and
dynamics of them
...analysed to identify
key features (red)
 The scenarios were converted to a
cognitive map and analysed to elicit the
particularly key features
Modelled using Decision Explorer
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Case Study
 A Bayesian Network
was produced from the
cognitive map for each
scenario
 Business experts finetuned the model and
provided evidence to
explain the states of
each node in the model
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Modelled using AgenaRisk
© 2011 Milliman
Case Study
 Factors which are present in
multiple scenarios are
explicitly connected
 Final loss distribution
obtained by adding
scenarios together
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Risk Monitoring with New Risk Metrics
 Using metrics designed to describe complex non-linear patterns,
you can see signs of trouble building up and begin to form
theories about the dynamics
 You can actually measure how much information something
contains:
I(x) = -log p(x)
 If something is surprising it will tell you a lot
 Looking at your management information in this way can yield
insights about the early development of unusual behaviours
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Connectivity
 Typical correlation measures cannot spot
non-linear dependency
 Mutual information sharing can
Different levels of correlation
Example
Q ~ U[0,2p]
R ~ U[4, 5]
X = R cos Q
Y = R sin Q
Sample of 1000
Correlation = 0.0
Mutual Info = 1.0
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Looking beneath the surface
This company’s performance seems complex, involving many variables
Same
outcome
but
different
drivers
Produced by
Milliman using:
This company’s performance seems less “complex”
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Emerging Risk
 Risk registers typically force the assignment of a label to each
entry
 But the entries are often not that simple
 By using a more granular labelling
approach it is still possible to aggregate
the information
 Technique from biology permits analysis of:
– Which entries are “like” each other
– Understanding of how risk scenario characteristics evolve
– Clues about potential future scenarios
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Evolutionary forces
 Application of Cladistics
 Developed in biology to permit
classification of organisms into
groups without prejudging what
the hierarchy of relationships
should be
 A simple technique gives a
much more realistic idea
about the risk profile of the
business
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Source: Milliman Risk DNA Analysis™
Risk Culture
 Systems view of risk culture looks at
– Structure of company’s communication
infrastructure (who is talking to who)
– Measure efficiency of info transmission
– Identify traits of company personality – key person risk
– Identify current position of company’s personality from different
perspectives
– Indicate current potential of company to achieve different levels from
different perspectives
– Develop plan to improve maturity of risk culture within the bounds of
what is possible
– Simple questions-based input, but...
– ...scientifically grounded in psychology, behaviourial science, social
network analysis and complex systems
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Australian Case Study
Section 3
Australian Industry Fund Case Study
 Hypothetical Australian industry superannuation fund
 Primary strategic objectives:
– Provide retirement savings and pension products and services that
meet member needs
– Maintain, enhance and protect their member value proposition
 Key questions:
– What are the most important drivers of the business?
– How complex is the business?
– How do the risks inter-relate and interact?
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Concept Map
Industry goal
Company goal
41 concepts
81 links
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What are the Drivers of the Business?
Top 10 concepts / business drivers
# immediate
links
Weighted
links
Retain existing members
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Risk and retirement product selection
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Provide attractive returns
7
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(Poor) Capital market conditions
7
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Ageing member population
7
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Maintain low fees
6
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Generate economies of scale
6
19
AUM size and growth
6
18
Effective operational and governance structures
6
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Member contributions
6
15
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Concept Map
Industry goal
Company goal
Critical
Potent
Standard
41 concepts
81 links
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Most Critical Business Driver - Retention
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Economies of Scale
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Identify Feedback Loops  Scenario tests
18 feedback
loops exist in
this business.
This is one of
them.
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Use to drive
scenario tests
around concepts
not immediately
obvious
© 2011 Milliman
UK Actuarial Profession Risk
Appetite Research
Section 4
Risk Appetite Research
 UK Actuarial Profession put out a call for research to provide
practical tools for creating a risk appetite framework and
emerging risk
 Milliman and the Universities of Bath and Bristol Systems Centre
delivered a set of tools leveraging complex systems methods
 It is hard to align operational risk limits to overall risk appetite as
the relationships are many and non-linear
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Why is Risk Appetite Complex?
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Risk Appetite Research
Break down high level risks into more
granular perspectives....
Balance
Sheet
Credit
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Market
Liquidity
P&L
Insurance
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Reputation
Operational
Risk Appetite Research
Risk appetites are linked to a series of
operational indicators whose level should
reflect the level of risk being taken
Explicit allowance for factors which relate to
multiple risks
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Risk Appetite Research
Bayesian Network used to identify
what state the indicators will be in if
the risk appetite levels are reached...
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Risk Appetite Research
Same model can be used to estimate
the risk level once current level of
indicators observed...
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Summary and Discussion
Section 5
Summary
 Studies confirm that modern society and its companies are
becoming increasingly complex
 The study of complex adaptive systems brings tools to help
understand and manage such systems
 Using techniques to understand “the system” makes it easier to
manage risks
 Think “outcomes” not “how”
 Frameworks need to be adaptive and able to cope with nonlinearity
 Don’t forget about the people
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Thank You!
 Questions?
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