From economic capital to ERM final

Dia 1

From Economic capital to ERMServaas Houben

DisclaimerThe views and opinions expressed in this presentation are those of the author and do not reflect the official policy or position of Prudential. Examples of analysis performed within this presentation are only examples. They should not be utilized in real-world situations as they are based only on very limited and dated open source information.

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AgendaEconomic capitalERMMaking the transition from economic capital to ERM

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Agenda Ecap4

Aim of the presentation is to show how stochastic scenario files are being created to calculate an SCR

Agenda Ecap5

Capital requirement under SII6

Own funds = assets liabilities

SII looks at 1 year time horizon

Focus on a single part of the distribution (worst case scenario)

Alternatives are P&L or expected profit per unit of capital requirementValue at Risk metric used to determine capital requirement over 1 year horizonBased on instantaneous stress (standard formula)Monte Carlo method to determine capital requirementScenario file creation based on estimation 1 year in the futureCVaR: conditional VaR (tail value at risk)ORSA applies multiyear horizon

Alternative measures are formula based or replicating portfolio

Although VaR is widely used, it has its limitations (sub-additivity: VaR of a combined portfolio can be larger than the sum of the VaRs of its components)

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VaR limitations - subaddivity7Risk 1ProbabilityLoss0.031 mln0.97095% VaR0

Risk 2ProbabilityLoss0.031 mln0.97095% VaR0

Risk 1 and 2ProbabilityLossOne event0.05821 mlnTwo events0.00092 mln95% VaR1 mln

Also VaR struggles to give insight when based on small samples: e.g. 100 simulations will give same value for 1 in 200/2000 Value at Risk

Example can be a policy which has a chance of 3% chance that the policyholder dies and hence payment is required7

QuizData:Monthly capital return index S&P 500 returns from Dec 1927-Feb 2011Dec 1927 index value 17.66, Feb 2011 1,327.22Total number of 998 monthly returns

8Question:When excluding 10 highest monthly returns (setting them to 0%) what would be the index value as at Feb 2011?

Answers1000

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ResultsSet highest 10 values to 0: 172.80 (-87%)Set lowest 10 values to 0: 15,330.78 (+1.050%)9

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Risk mitigation through dividends10

When including dividend return, the fall is only -40%

Downturns in 2002 and 200810

Overall scenario creation process11

Testing measures are sensitive to certain errors (e.g. largest deviation, square of large deviations)

Testing models by backtasting Validate calibration 11

Agenda12

Risk identificationIdentification of quantifiable risks Mapping of individual risks to homogeneous risk groupsDiversificationReportingTrade-off granularity and practical implementationRisk universe stores information13

Pillar 1 only considers 1. market risks, 2. non market risks, 3. operational risks

Example granularity: not modelling equity portfolio consisting of all stocks, but by using a benchmark (S&P)

Risk universe (list of risks) provides overview risks and mappingsAvoid overlap of the several risk (double counting)Avoid missing out on risks

Risk driver universe sign off essential in process to avoid rerunning of the process (Monte carlo is time consuming)13

Agenda14

Data selectionEmpirical data:Market risksNon empirical data/expert judgment:Operational risks Non market risksLife and non-life risks

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Real world scenario generator includes risk premium for certain asset classes Sampling error throughout the entire processPossibly assign more importance to more recent data

Data limitations:Equity: Market index not replicating insurance companies investment mix (basis risk)Capital and total return indices (dividend component)Limited data available for Asian economies (e.g. Vietnam only since 2001)Nominal interest rates:Government bond data: in general more historical data available (swaps more recent)Swap curves: not entirely risk free, but longer maturities available to calibrate toHow to adjust for high or low interest rate environments?Extrapolation of yield curves (Wilson Smith)LQP and MPProperty:Stale data due to quarterly or half yearly updatesEquity (land) and bond like (commercial) property available so differentiation requiredCredit:Plenty data available for US corporate credit riskLimited data available for non US credit risks: e.g. merrill lynch UK/EUR data since 1996, Itraxx since 2003Limited data for lower ratings and structured creditTransition probabilities complicate credit (modelling challenges)Operational risks:Databasis (ORIC): however scaling is requiredExpert opinion: frequency and severity combinationOutliers result in tail exposure: hence might overshadow other risk driver behaviourNon market risks:Limited data may be available to determine distributionNormal distribution might be simplest but leads to possible negative scenarios-flooring required?

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Nominal yield curvesRisk Management Hedging uses assets quoted on OISPricing (Guarantees) Funding for hedging based on OISProvisioning Solvency II based on LIBOR & UFROne-off surplus (based on current market environment)Hedging efficiency and provisioning risk due to LIBOR-OIS basisEUR 24 August 2012

Source: Bloomberg 16

L2 text changed LLP from 30 years to 20 years which is beneficial for the insurance industry

Spread componentsSpread = yield corporate bonds risk free yield Credit risk factors:Expected defaultSpread wideningDowngrade/transitionNon credit risk factors:TaxRegulatoryLiquidity17

Spreads:Spread widening: e.g. issue does not transfer to different rating class but spread increases compared to risk free government bondsDowngrade: has shown that after a downgrade, the credit spreads increase as investors require a higher reward for a perceived higher risk

Ratings:Market data: illiquidity, embedded options

Adjustment to risk free rate more significant in extreme scenarios where spread widens (more beneficial)Assume model portfolio on which credit shocks are appliedLQP used in QIS5 (2010) (calibration done on CDS basis approach):Different for each currencyDependent up until certain termMP in new level 2 text:No term limitPossible negative adjustmentQuality requirement to corporate bond portfolio to benefit from MP

17Fleming 25 January 2013 Amsterdam

Liquidity and Matching premium adjustments18

Source: Itraxx

Adjustment to risk free rate more significant in extreme scenarios where spread widens (more beneficial)Assume model portfolio on which credit shocks are appliedLQP used in QIS5 (2010) (calibration done on CDS basis approach):Different for each currencyDependent up until certain termMP in new level 2 text:No term limitPossible negative adjustmentQuality requirement to corporate bond portfolio to benefit from MP Model these risks by assuming corporate bond portfolio and use changes in credit spread to determine effect on MP and LQP18

Solvency II & Basel III spread assessmentSolvency standard formula currently II favours EEA government debt over other forms of sovereign debt (L2 text 2011)Basel III does not make this EEA/non EEA distinction

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Solvency II sovereign categorisationIssued in domestic currencyIssued in non-domestic currencyEEA government bondsNoneCapital chargeNon-EEA government bondsCapital chargeCapital charge

Separate treatment for CDS however exemption for EEA exposureIs equal treatment of EEA exposure justified? I.e. is default risk on a German (AAA) bond equal to default risk of Greece (CCC) bond?Why included in spread module and not in counterparty default module?Preference government bonds: AAA&AA rated exempted and lower requirements than corporates

Within solvency II ratings are used in the following modules:Spread risk no capital requirement for EEA gov bonds and AAA, AA rated government bondsConcentration risk (equity, property, spread) no capital requirement for EEA gov bonds and AAA, AA rated government bondsCounterparty risk (derivatives)

Philippines issues USD bonds and peso bonds


Differentiation EEA governments 1

Source: Bloomberg20

Differentiation EEA governments 2

Source: Bloomberg21

Differentiation EEA governments 322Source: Bloomberg


Why discounting matters - 1ILLUSTRATIVE EXAMPLE1 German and 1 Greek insurance companyBoth selling an annuity product65 year old maleLife annuity of 1000 paid end of the yearDutch mortality table 2010-2060No other (life) risksNew business ensures liability mix is stable over timeBalance sheet as per 30 June 2008German insurance company invests in German government bondsGreek insurance company invests in Greek government bondsBoth companies have surplus of 120% as at 30-6-2008Liability discounting on SII curves 23

Look at development of reserve requirement and surplus over timeFleming 25 January 2013 Amsterdam23

Why discounting matters - 2 24

German insurance company has stable solvency ratio over time (as discount rate assets and liabilities move in line)Greek insurance company suffers decrease in value in Greek government bonds, while liabilities have decreased less in valueFleming 25 January 2013 Amsterdam24

Why discounting matters - 3 25

German insurance company:Discounting under solvency II is beneficial as value of liabilities is lowerFleming 25 January 2013 Amsterdam25

Conflicts of interest government yield curveGovernment can avoid defaultIn case of default local regulator is under pressure to write down liabilities in line with the loss in value of local government bonds

Policyholder securityPressures government to reduce liabilitiesNational interest, stability, political motives26

Preferential treatment European government bondsDifference Basel II and Solvency II -> potential arbitrage opportunities?Possible change in discounting to comfort (Southern) European insurers?

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Default definition whats in a name?Dominic Republic 2005:Recovery rate on defaulted bonds 95%Rating agencies verdict: defaultZimbabwe 2006:Annual inflation rate: 1,216% p/aRating agencies verdict: no default Angola 1996:Annual inflation rate: 4,416% p/aRating agencies verdict: no default

27Source: Reinhardt & Rogoff

27Moodys definition of default risk:Missed payment (hence delayed payment Venezuela for a week is defined as a default)Debt restructuring

Reinhardt and Rogoff broaden the scope of default and also take into account hyper inflationFleming 25 January 2013 Amsterdam

Limitations rating agency dataLack of historical data: are the Probability of Default and Recovery Rate robust? Changing data set over time: historical average not reflecting future risksLack of transparency: how to compare different 1-year PDs?Difference local and foreign defaultsNo historical 1 year PD for investment grade (BBB and higher)Lowest rating is mixed bag

28 Source: Moodys

28PD: probability of default, RR: recovery rateRating agencies dont specify how they calculate their 1-year PD and RR values and how they adjust them for more recent events: how can different 1-year PDs and RRs from different rating agencies be compared?Difference local and foreign due to higher political and economical cost of local default. Gap has decreased over timeLowest rating consists of countries about to default or that have returned to the capital marketNo 1 year default data available, hence use formula to derive this

Fleming 25 January 2013 Amsterdam

Agenda29

Historical data collection30

Equity calibration consists of return, dividend and risk free rate30

Data amendmentsSelect indices deemed most appropriateApply transformation to data to check if data is stationary

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E.g.: lack of data then use same assumptions for bucket of countriesCurrent approach for stationary check is to plot transformed data over time period (visual check): could be an area to improve in the futureStationary requirement to fit distribution: if distribution over time changes (volatility) then we cannot assume historic data properly reflects future risks

Stationarity might happen in case of drift: e.g. some currencies are drifting over time due to monetary policy

Other stationarity tests exist (Dickey-Fuller and unit root test). Stationarity tests if there is no trend in the data (and hence you can use a distribution approach)

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CalibrationDetermine sample statsDistribution fitting: Fit to different distributionsIndividual country fitting/clustering Fit testing:Kolmogorov-Smirnov goodness of fit testAnderson DarlingSense test: 0.5% and 0.05% percentilesPlot sample data and fitted distributions

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Examples sample stats are moments (mean, variance, skewness and kurtosis) for each country and for MSCI world (weighted average taking into account diversification), and essential percentiles (1 in 200 down/up)Examples different distributions are Log gamma, Laplace, EGB2Overall fit is some kind of weighted average fitEquity clustering: developed markets (US, UK, HK), Europe, China, the restAdvantage of KS test is that it can be applied to any distribution. Disadvantage KS it takes only maximum difference into account. E.g. when extreme tails fits well, but body does not is that a problem for every risk driver?

Distribution fitting by method of moments or maximum likelihood

Sense testing: compare sample percentiles with distribution percentiles

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Agenda33

Case study - diversificationMeasurement of strength and direction of relationship 2 risk drivers:34

People apply diversification in their life (no exact specialisation as Adam Smith): study, exercise, hobbies, friends: if one goes wrong, you have something to fall back to

Tail events: when things go all wrong at the same time. E.g. remember a day when you woke up too late, missed your bus, arrived too late for the lecture etc.

Other challenges for estimation interactions between risks:Granularity: too granular, then lack of data. Not granular enough, combining risks which are not the sameData: stale prices, lack of historical data, changes in data due to different circumstances (regime shifts)Left and right tail dependency: risks might behave differently in positive or negative scenarios

FSA sceptical on correlation benefits due to banking crisis showing benefits evaporate in times of crisis34

Produce correlated risk drivers35

Uncorrelated risk driversCorrelated risk drivers

PSD requirement: might require manual adjustments35

Scenario productionApply correlated random numbers to calibrated distributionsApply restrictions to certain risk driversInterest ratesCredit spreadsVolatilities36

Agenda37

Capital aggregation

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Standard formula uses matrix manipulations to calculate capital requirementAnother option would be to include correlations within scenariosTake average of capital requirements around 1 in 200 scenario to avoid sample error38

Capital allocationPro rata approachMarginal VaR (Euler) approach:

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Traps and pitfallsOverlapping data introducing autocorrelationUsing instantaneous shock instead of using 1 year shock (including risk premiums)Not assessing parameter uncertaintyMis-using Monte Carlo40

Monte carlo sometimes used in operational risk or other risk frameworks when not required40

Curve fittingStrengthsWeaknessesQuick method and easy to runPath depending optionsFormula flexibilityApproximations may be requiredIncludes non market risksMany heavy model runs may be required

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Replicating portfoliosAssets matching liability cash flow pattern under a range of scenariosBased on no arbitrage principleStrengthsWeaknessesEfficient use of simulations (no nesting)Harder to incorporate non market risksLiabilities described in asset terms increasing transparencyMight be difficult to find actual or liquid asset for replication process Liability re-valuation not requiredHigher trading costs for illiquid assets

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Least square Monte CarloCombination of outer real world scenarios and risk neutral inner scenariosCombination of benefits of replicating portfolios and curve fittingStrengthsWeaknessesNo requirement to fit future cashflowsFast and accurate

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Break

Agenda ERM45


What is ERM?

Enterprise risk management (ERM)inbusinessincludes the methods and processes used by organizations to manage risks and seize opportunities related to the achievement of their objectives."process, effected by an entity's board of directors, management, and other personnel, applied in strategy setting and across the enterprise, designed to identify potential events that may affect the entity, and manage risk to be within itsrisk appetite, to provide reasonable assurance regarding the achievement of entity objectives."

What is risk?Uncertainty = absence of precise and complete knowledge leading to consensus of future state

Risk = state of uncertainty for a participant where some of the possibilities involve an undesirable outcome (e.g. loss)

Sharma report 2002identify and analyse the risks that have led to actual solvency problems between 1996 and 2001, or created a significant threat to the solvency of a firm (near misses), including any new and emerging risks; andprioritise each risk that has been identified;

Sharma report 2002 (2)Analysing the full causal chain improves supervisory practice:analysis showed that these causal chains began in each case with underlying internal causes, being problems with management or shareholders or other external controllers; these problems included incompetence or operating outside their area of expertise, lack of integrity or conflicting objectives, or weakness in the face of inappropriate group decisions

The Evolution of ERMRisk in non financial industriesCAUSE focusA desire to learn about where performance variation arisesLimited specific calculation of risk capitalProtect by having a big balance sheet

Risk in financial industryOUTCOME focusFocus on variability in capital requirements/usageLimited interest in operational factorsOnly hold sufficient capital to cover risks optimization processControversial

Why is ERM Challenging?Modern life/business is complexEach outcome is the result of many preceding interactionsERM requires knowledge of cause and associated effect

We are trained that reducing/simplifying a problem helpsOr, we assume that complex outcomes can only be generated by complex rules

It turns out that neither of these are true

Agenda ERM53


Understanding a CrisisSymptomsCausesSense-makingUnderstanding

The Company is an Open System

PropertiesHas a purposeEmergenceSelf OrganisationNon-linear behaviourCounter-intuitive and non-intended consequencesHas tipping point before collapseEvolves and history is importantCause and symptom separated in time and spaceComplicated systems are reduciblecomplex ones are not

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Risk Management

Need to constrain multiple inputs......producing multiple outputs to be kept within appetite...which flow through multiple complex adaptive interactions...It is essentially a large, complex multi-objective optimisation and control challenge

Understanding The System

Key NodesKey DriversGapsYou know more about how your company works than you might realise!

Combine and assess experts insights.Source: Milliman

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Sources of riskCausal models can help to represent complex dynamics between business indicators and business outputs

Business GoalsRisk sourcesBusiness indicators

Agenda ERM59


In The BeginningStrategyFundamentals Will my business achieve its goals?What could get in the way?How will it do so?Is there anything I can do about it?

Questions for the Board/ManagementHow certain do we need to be about success?Which problems do we know how to manage?Which problems should we avoid completely?

This is risk appetite

Goals for Risk ManagementRisk is relative to an observerMany different views of which risks to manageRegulator:Limit (impact of) company failuresCreate long term financial services marketEnsure products meet client needs and are understoodReduce systemic riskPrevent financial crimeCompany:Long term strategy deliveryAttract customersAttract investorsAttract staff

Investors:Stable returns on capitalTransparency about risks being taken

Different Perspectives (1)Focus on steady performance Company needs to ensure returns are consistentThis will tend to demand a framework which:Is seen as a positiveCan allocate risks by line of businessIntegrates risk and pricing to ensure risks are being rewardedFocuses carefully on capital allocation

Different Perspectives (2)Focus on avoiding failure More about balance sheet and internal controlsThis will tend to demand a framework which:Is perceived as a prevention structureConservatively sets a regulatory hurdle for capital levelsIs more focused on evidence of controls

Balancing The PerspectivesMost companies have to balance views due to different stakeholder requirements

Managing Which Risks?ERMComplexity of modern business demands holistic approachEmergence of ERM as a way to study and manage business-wide riskDifferent mind-set and tools needed vs traditional risk managementWider use of stress and scenario testingBetter integration with strategy and business processesLiabilitiesAssets

Agenda ERM66


Op PlanRisk Framework ComponentsBoardCEORisk Management SystemRisk CommitteeAudit CommitteeRisk Strategy

Risk Appetite

Internal AuditStrategyInternal Control System

Risk PoliciesRisk ProcessesControl Cycle, Modelling, Scenarios, Emerging Risk, Risk assessment

Design FactorsWhat are you trying to achieve?How mature/stable is the organisation/environment?How independent do you want to/can you be?What do others expect of you?

Organisation Design

Risk Management SystemRisk strategyIdentify the risks inherent in strategyDefine overall approach to risk acceptance, risk managementSets the tone from the top

Risk appetiteRisk preferences: Which risks do we seek to manageWhich risks do we accept (as a consequence)Which risks are unacceptableRisk tolerances:What are the boundaries of the uncertainties we will accept

Risk Management SystemRisk limitsWhat are the boundaries to our operational activity such that our risk appetite is met?

Risk policiesFor each broad source of uncertainty:How should we identify it?How should we assess it?How should we manage it?How should we monitor and report it?What are the relevant responsibilities for everyone?Guidance to help business manage the risk

Risk Management SystemRisk ProcessesOperational implementation of risk policiesUsing whole organisation to manage risk

Internal Control SystemDelegated authoritiesControls and limits

Agenda ERM73


Capital Risk AppetiteSpecify:The level you want to be most of the timeSay equivalent to a rating of A to AAThe level you can tolerate falling toSay, equivalent to a rating of BBBAcceptable frequency of falling from top to bottomSay, 1:25Target Capital positionMinimum tolerable Capital position

Risk Appetite isAn expression by key Stakeholders of the uncertainty they are prepared to accept in meeting their goals:

the degree of comfort and preference for accepting a series of interconnected uncertainties about achieving corporate goals.(Actuarial Profession: Allan, Cantle, Yin, Godfrey, 2012)

Fundamentally related to strategy, goals and rewardsA confusing phrase meaning different things to different peopleDefine what you mean and use the concept in your terms

Risk Appetite Concepts

Planned ResultPossible ResultsRisk CapacityOver performanceRisk ToleranceRisk Budget

What are you trying to achieve?What range of outcomes could happen? (hopefully bounded but not necessarily!)What range of negative variation could you absorb?What range of negative variation will you tolerate? (Note: some people prefer to use tolerance for putting up with risk I dont want and appetite for the active taking of risks you do want)You can choose to allocate resources to a level of risk which is no greater than your risk capacity. Tactically you could choose to set a budget greater than your tolerance for a short period76

Framework ElementsStrategic GoalsWhat are you trying to achieve?Financial goalsProfitamountProfitstabilityMembership returnsamountMembership returnsstabilityCapital coverageDimensionsStatutory viewEconomic viewRating agency viewTimeframeOrganisation level

Non-Financial goalsReputationCSRMarket positioningGrowthGoals may conflict

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Framework ElementsPossible OutcomesUnderstanding the strategy What are the boundaries to your outcomes?What mechanisms drive different regions of performance?Which are endogenous / exogenous?Which are unique to you / common to peers?

Your strategic (ORSA) analysis should contain much of what you need

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Framework ElementsAppetite Statement(s)Understanding uncertaintyThe plan should be the expected result (mode)Risk appetite about understanding distribution around thatStatistical description of results not good enoughyou need to know the drivers

Risk appetite is not just a capital calculationit establishes criteria for business management

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Framework ElementsCapacityHow much resource do you have available to absorb uncertainty?What type of resource do you need to absorb uncertainty?Is the resource fixed or variable? Over what timeframe?What could make your resource diminish?Is the strategy consistent with your capacity for risk?

Capacity has to be there when you need it !!!

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Framework ElementsPreferences / ToleranceWhat are the sources of uncertainty in your goals?Which will youSeek for reward?Accept? Try to avoid altogether?How much of each source of uncertainty will you tolerate?How often could you tolerate reaching that level of performance?

It is important to recognise the different reasons for taking riskit should influence your appetite!

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Framework ElementsBudgetHow will you set your risk budget for each risk class?Align resources to areas where risk is rewardedBudgets can be used to authorise tactical opportunities even if appetite temporarily exceededAlso helpful in approving/encouraging tactical variations at lower levels

Budgets are a good way to encourage risk taking

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Framework ElementsLimitsOperational activity should beEncouraged to take risks up to the budgeted levelConstrained not to cause risk appetite to be chronically/accidentally exceededFocus on outcome-based metrics wherever possibleWatch for adaptation/emergence issues Linking operational limits to overall appetite is HARD

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Design ConsiderationsAppetite Statement(s)A good statement set explains:The desired tone of risk management (risk culture) The risk outcomes that are not/acceptableThe risk-taking practices that are/not acceptable

Reflect on discussions at business performance reviewsWhat do people focus on?At what level of performance do people get upset?Are there any behaviours you are looking to change?

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Low appetite for reputational risk exposureDesign ConsiderationsAppetite StatementsEarnings at risk no higher than X% of 1-year planned earningsX% average annual growth in EPSMaintain ratings at current levelCapital at risk not to exceed X% of available financial resources over Y-year period with 1:Z frequencyLoss from single event less than XMaintain sufficient capital to meet all obligations at confidence level XWill not engage in activity threatening long-term value

Design ConsiderationsRisk Appetite Statement(s)Risk appetite statements must be clear and understandableShould be clear why it is includedShould be clear which risks are sought/accepted/avoidedShould be clear what you are getting rewarded forStatements relating to levels of risk must be measurableShould be clear what the desired and tolerable performance levels areQualitative statements must be assessableClear link from appetite to strategy

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Design ConsiderationsCascading AppetiteConsider relevance of statements for each levelHow are operational plans expressed?What are unit leaders judged on? By you? By regulator?Which measures bite at lower levels?Consider practical aspectsCan units achieve granularity at speed?How will unit results be aggregated at the top?Appetite statements must be consistently translated

Design ConsiderationsCascading AppetiteDecide how to cascade top level appetite to unitsWhere do you want diversification to reside?Allowance for real dynamics between levels influencing riskFungibilityManagement actionsRegulatory constraintsA big challenge is to know how the parts add up to the whole

Risk Appetite Components

Planned outcome Tolerated outcomeFrequency Preferences

Sources of uncertainty Objectives

Business drivers

Adaptation and emergence make this hard

Final ThoughtsRisk appetite concept is fundamentally aboutKnowing how your outcomes might varyDeciding how much variation is acceptableKnowing why your outcomes varyDeciding which whys are un/acceptableLimit framework is aboutUnderstanding how operational activity influences outputReacting to adaptation and emergenceYou cannot control a complex adaptive systemResiliencenot the same as padding

Agenda ERM91


ToolsIdentificationPresentations of dataTrying to spot patternsPlots of trends, calculations of risk metricsQualitative measuresAssessmentModels to show variation in outcomesScenariosReverse stressQualitative understanding of dynamicsRisk registersConnectivity of risk factors

Capital ModellingCash flows projected according to set of assumptionsThousands of scenariosManagement actions / policyholder behavioursInteractions between assets and liabilitiesShows distribution of outcomes

Cognitive Mapping - Its all in your head!

Key Nodes

Key Drivers

GapsSource: MillimanPeople 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.

Agenda ERM95


How their frontline supervisor behaves and/or how she might respond to the same issue;How their peers are acting; andTheir own moral compass.

When faced with an ethics, compliance, or risk-related decision, the following are considered:

(and in this order)

Culture is a unique emergent property of an organising system of human activity. Its dynamics are not revealed by adding up the collective traits of individual members.

M Doyle, the former head of litigation at DuPont and a well-known compliance and ethics professional,Several different studies show that when people are faced with an ethics, compliance, or risk-related decision, they consider the following (and in this order):In other words, culture matters - more than a personal set of values; more than organizational values; more than risk management processes, and supporting technology.

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Typical Operational Risk Modelling Process

Typical Problems

Hard to know/prove you have coverage

Difficult to capture expert knowledge

Loss distributions hard to describe and evolve

Hard to determine correlations between risks

Monte Carlo needed to produce final loss estimates

Difficult to link model with business drivers

Different Approach for Complex Situations

Statistical models, assuming constant driversRegisters assuming single characteristicsScenarios imaginedEmerging risks by spotting events

Models based on system driversDescriptions of risk profile taken holisticallyScenarios derived from risk profileEmerging risks spotted early from system

Operational risk is difficult because people look at it through the wrong lens

Bayesian Network ExampleOversleep

P(Yes)=0.1 / P(No)=0.9Arrive Late

P(Yes|Osleep)=0.8 / P(Yes|Not Osleep)=0.3P(No|Osleep)=0.2 / P(No|Not Osleep)=0.7

Prior estimate that someone oversleeps 10% of the time and arrives late 35% of the time

If we know they arrived late, we increase our estimate that they overslept to a 23% chance

Case StudyUsing a Bayesian Network to model losses arising from operational risk

Agenda EC -> ERM102


Making the transition from Ecap to ERMIntegration of calculation in the Risk Management cycleUsage of the results in the decision taking processNeed for understandable and up to date information at every momentTranslation into key risk indicators

Risk management cyclesSolvency II;Article 44RISK

Examples

Examples

TARGETThe Evolution of ERMFuture ERMDownside protection vs. Risk-reward optimisationSingle risk vs. Multi riskBalance sheet vs. Governance

ERM cannot make the final integration with business without evolving to embrace complexity

Future ERMEmbracing complexityHolistic perspectiveNon-linearityRisk interactionsUnderstanding causes not just outcomesResilienceSystems thinkingView the problem from multiple angles

Biographies

Servaas Houben heads the risk scenario generation team at Prudential, London. He studied econometrics in the Netherlands and worked in life insurance for the first four years of his career. Following this, he worked in Dublin and London. Besides actuarial, Servaas completed the CFA and FRM qualifications, and regularly writes on his blog, for CFA digest and Dutch actuarial magazines.

Email: [email protected]: http://actuaryabroad.wordpress.com

BiographiesHenny Verheugen is a principal at Milliman in Amsterdam. He serves insurance companies with the development of their risk management and valuation frameworks. He studied actuarial science and worked for insurance companies and consulting firms. Besides actuarial, Henny studied accountancy.

Email: [email protected]

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From economic capital to ERM final