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Improving Risk Management Unravelling the complexity of risk Institute of Actuaries of Australia ERM Seminar 20 September 2011 Neil Cantle Joshua Corrigan.

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Presentation on theme: "Improving Risk Management Unravelling the complexity of risk Institute of Actuaries of Australia ERM Seminar 20 September 2011 Neil Cantle Joshua Corrigan."— Presentation transcript:

1 Improving Risk Management Unravelling the complexity of risk Institute of Actuaries of Australia ERM Seminar 20 September 2011 Neil Cantle Joshua Corrigan

2 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

3 Complex Systems Framework for Risk Analysis Section 1

4 4 © 2011 Milliman 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 4 © 2011 The Actuarial Profession

5 5 © 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 5 © 2011 The Actuarial Profession

6 6 © 2011 Milliman 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

7 7 © 2011 Milliman Understanding a Crisis Symptoms Causes Sense-making Understanding

8 8 © 2011 Milliman 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)

9 9 © 2011 Milliman 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

10 A New Toolset for Complex Risk Analysis Section 2

11 11 © 2011 Milliman Cognitive Mapping - Its all in your head! Key Nodes Key Drivers Gaps 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.

12 12 © 2011 Milliman 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

13 13 © 2011 Milliman 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

14 14 © 2011 Milliman Case Study Factors which are present in multiple scenarios are explicitly connected Final loss distribution obtained by adding scenarios together

15 15 © 2011 Milliman 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

16 16 © 2011 Milliman Connectivity Typical correlation measures cannot spot non-linear dependency Mutual information sharing can Different levels of correlation ~ U[0,2 ] R ~ U[4, 5] X = R cos Y = R sin Sample of 1000 Example Correlation = 0.0 Mutual Info = 1.0

17 17 © 2011 Milliman Looking beneath the surface Produced by Milliman using: Same outcome but different drivers This companys performance seems less complex This companys performance seems complex, involving many variables

18 18 © 2011 Milliman 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

19 19 © 2011 Milliman 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

20 20 © 2011 Milliman Risk Culture Systems view of risk culture looks at –Structure of companys communication infrastructure (who is talking to who) –Measure efficiency of info transmission –Identify traits of company personality – key person risk –Identify current position of companys 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

21 Australian Case Study Section 3

22 22 © 2011 Milliman 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?

23 23 © 2011 Milliman Concept Map Industry goal Company goal 41 concepts 81 links

24 24 © 2011 Milliman What are the Drivers of the Business? Top 10 concepts / business drivers# immediate links Weighted links Retain existing members1022 Risk and retirement product selection821 Provide attractive returns719 (Poor) Capital market conditions717 Ageing member population716 Maintain low fees618 Generate economies of scale619 AUM size and growth618 Effective operational and governance structures616 Member contributions615

25 25 © 2011 Milliman Concept Map Critical Potent Standard Industry goal Company goal 41 concepts 81 links

26 26 © 2011 Milliman Most Critical Business Driver - Retention

27 27 © 2011 Milliman Economies of Scale

28 28 © 2011 Milliman 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

29 UK Actuarial Profession Risk Appetite Research Section 4

30 30 © 2011 Milliman 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

31 31 © 2011 Milliman Why is Risk Appetite Complex?

32 32 © 2011 Milliman Risk Appetite Research Balance Sheet P&LReputation CreditMarketLiquidityInsuranceOperational Break down high level risks into more granular perspectives....

33 33 © 2011 Milliman 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

34 34 © 2011 Milliman Risk Appetite Research Bayesian Network used to identify what state the indicators will be in if the risk appetite levels are reached...

35 35 © 2011 Milliman Risk Appetite Research Same model can be used to estimate the risk level once current level of indicators observed...

36 Summary and Discussion Section 5

37 37 © 2011 Milliman 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 Dont forget about the people

38 38 © 2011 Milliman Thank You! Questions?


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