Improving Risk ManagementUnravelling the complexity of risk

Institute of Actuaries of AustraliaERM Seminar

20 September 2011

Neil CantleJoshua Corrigan

2 © 2011 Milliman

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

Complex Systems Framework for Risk Analysis

Section 1

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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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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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Information Theory

A New Perspective on Risk

Bayesian networks

Psychology

Graph theory

Complex systems

Systems dynamics

Behavioural science

Cladistics

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

Cognitive mapping

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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

A New Toolset for Complex Risk Analysis

Section 2

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Cognitive Mapping - It’s all in your head!

Key Nodes

Key DriversGaps

Source: Milliman

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.

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Case Study

UK Life Assurer had a series of operational risk scenarios which were monitored regularly and had been modelled as loss-distributions

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

The scenarios were converted to a cognitive map and analysed to elicit the particularly key features

Cognitive map of scenario description

...analysed to identify key features (red)

Modelled using Decision Explorer

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Case Study A Bayesian Network was

produced from the cognitive map for each scenario

Business experts fine-tuned the model and provided evidence to explain the states of each node in the model

Modelled using AgenaRisk

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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 canDifferent levels of correlation

Q ~ U[0,2p]R ~ U[4, 5]X = R cos QY = R sin Q

Sample of 1000

Example

Correlation = 0.0Mutual Info = 1.0

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Looking beneath the surface

Produced by Milliman using:

Same outcome

but different drivers

This company’s performance seems less “complex”

This company’s performance seems complex, involving many variables

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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

Source: Milliman Risk DNA Analysis™

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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

Australian Case Study

Section 3

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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 MapIndustry 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 10 22

Risk and retirement product selection 8 21

Provide attractive returns 7 19

(Poor) Capital market conditions 7 17

Ageing member population 7 16

Maintain low fees 6 18

Generate economies of scale 6 19

AUM size and growth 6 18

Effective operational and governance structures 6 16

Member contributions 6 15

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Concept Map

Critical

Potent

Standard

Industry goal

Company goal

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.

Use to drive scenario tests

around concepts not immediately

obvious

UK Actuarial Profession Risk Appetite Research

Section 4

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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

Balance Sheet P&L Reputation

Credit Market Liquidity Insurance Operational

Break down high level risks into more granular perspectives....

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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...

Summary and Discussion

Section 5

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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 non-linearity

Don’t forget about the people

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Thank You!

Questions?