Advancements inImplementing Operational
Risk, Stress Testing andRisk Appetite for ORSA
Institute of Actuaries of Japan
Joshua Corrigan
Principal, Milliman
5 March 2014

Contents
1. The Evolution of ERM
2. Technical Developmentsa) What you knowb) What you seec) Relationships

3. Applications:a) Operational Riskb) Risk Appetitec) Stress Testingd) Interest Rate Risk

2

© 2014 Milliman

The Evolution of ERM

Section 1

The Evolution of ERM
Explanation

Prediction

RiskManagement

4

ERM 1.0

ERM 2.0

ERM 3.0

The Dawn of Risk
Management

The Era of ALM andFinancial Risk

The Era of Reward
and Governance

The Era of Culture
and Resilience

Basic analysis ofindividual risk types
Attempts to understandvariation in key factors.

Development of ALM tocapture balance sheetinteractions.

Perspective of risk as
related to reward.

Embedding of risk andunderstanding it throughculture.

ORSA

Acceleration of financial
risk techniques.

Concept of risk appetite.

Resilience concept
emerges.

Results aggregated to theenterprise level.

Focus on management aswell as measurement.

Focus on risk dynamics
and interrelationships© 2014 Milliman

ORSA Becoming a Global StandardWhat business decisions do we need to take given
we are exposed to risk and uncertainty?

Risk

Uncertainty

P&L and Balance Sheet
Modelling

Scenarios

5© 2014 Milliman

BusinessDecisions
EmbeddingORSA: Balance Sheet Risk Management
Components and Inter-relationships
SurplusCapitalOwn
Funds

Risk Appetite Process
Operational

Economic
CapitalLiquidity
Counterparty

MarketValue ofAssets

Mortality
Morbidity

Reserves
MarketConsistent
Value of
Liabilities

Behavioure.g. persistency
Demographics

CreditVolatility

Marketlevels

Interest rates

ALM Process6© 2014 Milliman

ORSA: P&L Risk Management
Understanding the drivers of P&L uncertainty
Sales
Alignment of incentives b/w
customer, distributor, manufacturer
 Persistency risk, churnDistribution
Costs
Manufacturing
Costs

Operational capacity, availability,
utilisation, productivity
Economic, market drivers

7

Quality of u/w and pricing process
Experience of demographic,
market and behavioural risks

ExpenseCosts
Operational Risk
Costs

Earnings and margin uncertainty
driven by complex interaction
between above factors

Sustainability of growth
Quality of business Multiple interrelated drivers ofdemand (e.g. behaviour, economics)

Operating ProfitMargins© 2014 Milliman

Direct hit to the bottom line
Need to manage these risks (e.g.
fraud, mis-selling, mis-pricing)

Prediction ≠ Explanation
Need to move from pure statistical to causal risk frameworks

Which different
events could
cause me tolose thismuch?

8© 2014 Milliman

Complexity / Connectivity / Emergence
WEF Global Risks Map 2013

Complex systems mean you can’t
understand the whole by only
studying the sum of the parts.
It is the inherent and dynamic
relationships between risks, causal
drivers and outcomes that is key.
Simple measures of dependency
such as linear correlation are
typically misleading
Risks relating to complex adaptive
systems exhibit emergent
properties

9© 2014 Milliman

Current and Emerging Challenges

ERM 2.0

 Risk governance Risk processes Operational risk
 ORSA pillar 2––––––

Strategic / holistic risk assessment
Operational risk
Risk appetite
Scenario / stress testing
Risk interdependencies
Risk reporting Operational risk systems

10

ERM 3.0

 Resilience Risk culture Behavioural risks
 Emerging risk
 Reverse stress testing Risk dynamics and inter-relationships
through systems science Causal light models focused onexplanation, not just prediction
 Risk engagement with business Integration of predictive analytics© 2014 Milliman

Technical Developments
What you know

Section 2aCompanies are Complex Adaptive Systems
Risk is an undesirable outcome of a complex system

Traditional Risk Management Frameworks
Statistical models, assuming constant drivers
Registers assuming single characteristics
Scenarios “imagined”
Emerging risks by spotting events

Frameworks based on complex systems
Descriptions of risk profile taken holistically
Scenarios derived from risk profile
Models integrate all types of information
Emerging risks spotted early from system

Risk management can be hard if looked at it through the wrong lens12

© 2014 Milliman

Data is only part of the information set
interactions
InputsOutcomes

The System: information

What you see: data

What you know: judgment

13© 2014 MillimanDescribing the System“If the data was lost by a partner there
would be contractual issues to resolve which
would strain the relationship and there
would be damages to claim. This could
cause a loss of confidence in the partner
themselves..”14© 2014 Milliman

Cognitive Analysis
Produce a“minimallycomplex”
summary
Identify
unfinishedexplanations
more clearly

Ultimatelyconnected tomany nodes

Nodes which lead tomultiple highly
connected nodes

Immediatelyconnected to manynodes

15© 2014 Milliman

Find the mostimportantelements of the“system”

What are the Risks to the Actuarial Profession?

1627 February 2014

© 2014 Milliman

Relative Importance of Risk Drivers

17© 2014 Milliman

Deriving the Full Risk Profile
Forced Outage Causal Drivers

 Dominated by asset management drivers and other potent drivers Note multiple feedback loops: e.g. Quality of people, maintenance strategy

18© 2014 Milliman

Technical Developments
What you seeSection 2b

Using Data to Move Beyond Point Estimates
What do key risk indicators (KRIs) tell us about the likelihood of eachtype of risk outcome?
“There is a 5% chance ofno occurrence”

KRI 1“There is a 40% chanceof impact size A”“There is a 25% chance ofimpact size B and C”“There is a 5% chanceof impact size D”

KRI 220© 2014 Milliman

Causal Modelling with Bayesian InferencePrediction with Explanation
Causal modelling techniques can be used to formally demonstrate how indicators flow through to thebusiness outcomes being studied. Framework retains the dynamic links between causes and losses
so risks are viewed in context and incorrect conclusions from silo-thinking are avoided.

Minimally Complex
View

Translate
minimallycomplex map
into causal
model

21

Causal Model

Enhanced Analysis

Calibrate
Bayesian
Network andcarry out
analysis© 2014 Milliman

A Bayesian Approach Bayesian networks are a method which can integrate dependencies
directly between trigger events, risk drivers, and consequences
 Simultaneously assess all levels of outcomes (profit, capital)
 Can think of the prior as the “theory”, and the evidence as “observation”– All scientific fields use Bayesian statistics, so why don’t we!

P( A, B )  p ( A / B ). p ( B )P( B, A)  p ( B / A). p ( A)p( B / A). p ( A) P( A / B) p( B)

22

whereP(A) is the prior
P(A/B) is the posterior
P(B/A)/P(B) is the evidence© 2014 Milliman

What is a Causal Model?
 A causal model is one
which conditions
outcomes directly upon aset of interrelated causalfactors
 Causal factors are defined
directly in terms ofbusiness language It captures the complex
web of interrelationships
and dependencies directlyfrom the outset

23© 2014 MillimanSimple BN Case Study - Flood Model Outcomes:– Prob(Flood) Risk indicators– Rain (forecast)– Dam levels (avg) Risk mitigants– Quality of flood
defenses (measurablebut uncertain)
Source: AgenaRisk

24

27 February 2014

© 2014 MillimanRisk Monitoring Prior water = medium
 Risk level changes as
the states of causal
drivers change Consistent states of other variables calculated using Bayesian inference
25

27 February 2014

© 2014 Milliman

Reverse Stress Test
 Flood = 100%
 What does thesystem look like?
 Bayesian inference
used to resolve
states of related
drivers
 This is how weresolve risk
appetitestatements into
consistent risk
driver limits
26

27 February 2014

© 2014 Milliman

Technical Developments
Relationships

Section 2c

Unsupervised vs Supervised Techniques
Derivation of rules / algorithms tosearch data to uncover correlations
and patterns

Human judgment required to either
structure the analysis or as aninformation source itself

 Decision trees

 Linear multifactor regression

 Random forests

 Conditional / Bayesian probability Neural nets

 Non-linear copulas

 Nearest neighbours

 Cognitive mapping Support vector machines

 Bayesian networks

 Cluster modelling Phylogenetics

 Mutual information

 Network analysis

28© 2014 Milliman

Data Analytics
Data is a key strategic asset, but only part of the solution

Prescriptive analytics

Automatically prescribe
and take action

Predictive analytics

Ex-ante scenarios

ReportingDescriptive analytics

Ex-post analysis

Processing

Data preparation

Indexed, organised andoptimized data

Big Data

Supply of multiple
different data sets

Structured andunstructured data

Increasing Value

Analytics

29© 2014 Milliman

Information Theory Shows us the Way
 Perhaps the most critical question in risk management:“Do I have any information upon which to condition an outcome / risk
driver etc. and what quality level do I place on it?” Information theory concepts:– Entropy: quantifies the uncertainty involved in predicting the value of arandom variable– Mutual information: quantifies the amount of information in common
between two random variables

I(x) = -log p(x)
 In light of no other information, the principle of maximum entropy
applies: all outcomes are equally likely
30© 2014 Milliman

Connectivity – Capturing Non-Linearity
 Typical correlation measures cannot spot
non-linear dependency
 Mutual information sharing canDifferent levels of correlation

Example ~ U[0,2]
R ~ U[4, 5]
X = R cos 
Y = R sin 
Sample of 1000

Correlation = 0.0Mutual Info = 1.0

31© 2014 Milliman

Assessing Network Connectivity & Complexity
Non-linear measures of dependence are critical2003

2004

2005

Peak of complexity

 Complexity changes significantly over the year, with several of the key drivers changing
between 2003 to 2005
Produced byMilliman using

32© 2014 Milliman

Aggregate Loss – Dependency Structure A profoundlydifferent way ofaggregating risks
 Diversification at allparts of the loss
distribution can now
be explained by thestates andinterrelationships ofbusiness drivers
 No need for abstract
correlations,
copulas33© 2014 Milliman

Correlation from Cause
Validating Dependency Structures
 Correlations measure a degree of co-variation. You can determine this co-variation forcomplex phenomena by using causal models of their dynamic relationships.
 The models more naturally allow for an understanding of regime shifts in behaviours andallow you to meaningfully stress dependency parameters used in other models.

34=© 2014 MillimanApplications
Operational Risk

Section 3a

Milliman Research
Report 2013

Risk - Failure to recruit, retain and develop staff
Cognitive Map Analysis

 Key concepts:– Impacts: customer service worsens, quality of work deteriorates– Drivers: failure to provide adequate staff training, unmanageable
work volume, failure to align staff with business needs– Controls: staff appraisal process, performance managementprocess Map properties
Property36© 2014 Milliman

Check

Links: Nodes ≥ 2 : 1

Hyperconnectivity



Heads (% of Nodes)

3%"Heads" all impacts?

"Tails" (% of Nodes)

21%

Free nodes?

Loops?

Risk - Failure to recruit, retain and develop staff
Risk Quantification using a Bayesian Network Aggregate Loss:––Mean: Xm
99.5%: Ym

 Sources of loss: Unbudgeted
recruitment, training and resourcing
costs
 Business inputs: Resourcing , Training
and Recruitment budgets
 Sensitivity Analysis:37© 2014 Milliman

Operational Risk Capital80

1.83 1.53

Unique

60

36.52

40
20
0

5.34

1.28 1.16
14.26
1.82 1.56

4.83

4.30
45.13

Common70

1.831.53

60
50

29.29

24.66

27.41

40

4.30
4.83
1.291.16
20
15.74
10
1.57
2.73
5.51
030

38

© 2014 Milliman

37.74

Assessing Extreme Risk Events
Rogue Trader Scenario

39© 2014 MillimanApplications
Risk Appetite

Section 3bPeter Clark award forbest paper in 2012 inUK Profession

Primary Risk Appetite Challenge:
Aggregating / cascading RAS thresholds  risk limits
Knowing how these...interact...

...to produce these

It is essentially a large, complex multi-objective optimisation and control challenge
41© 2014 Milliman

Risk Appetite Components

 Planned outcome
 Tolerated outcome
 Frequency
 Preferences

Sources of
uncertainty Businessdrivers

Adaptation and emergence make this “hard”
42© 2014 Milliman

Objectives

Business Objectives Linked to Risk Sources
 Risk Sources: Contribution of risk source tooverall risk set from:– Market– Credit Counterparty Default– Capital analysis– Liquidity– Profit analysis– Underwriting– Expert judgment– Operational43© 2014 Milliman

Identify Sources of Uncertainty for Each Risk
 Credit: Market:– Reinsurance counterparty– Equity– Distribution counterparty– Credit spreads– Derivative counterparty (orclassified under market)– Inflation– Foreign exchange– Interest rate44© 2014 Milliman

Model now links business objectives tosources of risk and indicators

 Capture multiple influences: operational risk in particular links tomore than one risk characteristic45© 2014 Milliman

Setting Risk Appetite
 Use propagation properties of Bayesian Networks
Setting an outcome here…

…tells us what the states ought to be here46© 2014 Milliman

Propagating Evidence Down
 Setting desired appetite levels
at the top, translates into
information about underlying
limits e.g. counterparty credit47© 2014 Milliman

Monitoring Risk Levels Against Appetite
 Use propagation properties of Bayesian Networks…gives us an estimate of risk levels here

Entering observed values here…

48© 2014 Milliman

Propagating Evidence Up Entering actual observed
indicator values gives
information about risk levels
versus appetite49© 2014 MillimanApplications
Stress Testing

Section 3cAward for “Practical Risk
Management Applications”
at ERM Symposium 2013

Stress / Scenario Testing:Overload But Incomplete
They are actuallyspecific examples
contributing to theaggregate loss of type X

These are lots of different
variations we thought of for
how loss type X could
happen…but so arethese that wedidn’t think of!

51© 2014 Milliman

Codifying Business IntelligenceCognitive Mapping & Analysis

2. Connectivity analysis
identifies key features ofrisk system

3. Collapsed view providesa ‘minimally complex’description of the system

1. Detailed notes from each
workshop used to translate
the risk discussion into
cognitive maps
52

separate cognitive maps
merged together to givecomplete description of risk
profile© 2014 Milliman

Retains the key features
necessary to understanding
drivers of uncertainty

Identifying Critical Drivers
Highly connected drivers across the various silos Structure of the map broadly reflects
the key areas discussed within theworkshops

Reputation
Financial
ProductBank SalesChannel–Financial, Agent Channel, Product,
Customer, Reputation, External,
Bank Sales Channel

 Visually represents the distinct risk
profile of each sales channel

Customer
AgentChannel

External

Cognitive analysis
identified key
interactions between
the risk profiles

53© 2014 Milliman

Qualitative Scenario Creation
Understand full narrative of causes to consequences“The life company does not deliver effective agent
training with respect to current regulation, industry
best practice, and product knowledge. This leads toa gradual decline in the ability of sales agents tooffer compliant advice and meet required sales
standards.
Out-of-date and incomplete sales advice leads toincreased incidence of product mis-selling acrossthe business’s product offering.
A build up of customer complaints is picked up byindustry press and the regulator decides to review
current sales practise.”

1. Minimally complex view ofthe system studied to identify
interesting pathways between
concepts

54

2. Pathways used as a framework forthe scenarios, with additional
context included from the fullcognitive map© 2014 Milliman

All Shapes And Sizes

The transition from A to B
will be sudden not smoothA55© 2014 Milliman

BRecovering Scenarios
Travel Disruption

Pandemic

Reverse Stress

Civil Unrest

56© 2014 MillimanApplications
Interest Rate Risk

Section 3d

Causal Factor Explanation of Corporate Bonds
Corporate Bond Total Return Decomposition by Year and 10 Year Annualised
2520

15

10502001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

‐5‐10Real Cash Rate

58

Realised Inflation

Cash Duration RP

Nominal Duration RP© 2014 Milliman

Credit Spreads

Total Return10 Years

Long Term US S&P Return Decomposition
25
21.5
20

18.9

18

17.2
15

10.2
107.7

8.9

5.8

50.1

0‐1.7
‐5‐10‐15

1927‐29

1930‐39

Dividends

1940‐49

1950‐59

EPS Growth

1960‐69

1970‐79

P/E Change

Source: Priest et al (2007)59© 2014 Milliman

1980‐89

1990‐99

Combined Effects

2000‐05

Total Return

1927‐05

Eliciting the Causal System Structure toUnderstand Inter-relationships
10 Nominal Govt Bond
Return8 Inflation Indexed
Bond Return

6 Nominal CashReturn

11 Nominal Govt
Yields9 Real Govt Yields
4 Cash Duration RP
5 Nominal Cash Rate

3 Real Cash Rate

7 Real Duration RP

2 Expected Inflation

15 Nominal Corp BondReturn

14 Nominal CorpYield

17 Default Losses

13 Breakeven
Inflation

16 Credit Spread RP

12 Inflation
Duration RP

36 Swap Rates
24 Volatility

35 Derivative Pricesand Returns

23 Market sentiment

33 Valuation Level

25 P/E Ratio

1 GDP

31 Property Return
32 Rental Yields

34 Demand / supplybalance

19 Investment

27 Cyclical Earning
Growth20 Net Exports
28 Corporate
Profitability

26 Sustainable
Earnings Growth

18 Consumption
29 Dividend Yield

21 Productivity

60

22 Employedworkforce© 2014 Milliman

30 Equity ReturnModelling Full Dynamic Risk FactorDistributions
 Condition uncertainty in key
capital market variables uponrisk factors / drivers(subjective & objective)

61© 2014 Milliman

Disclaimer

This presentation has been prepared for illustrative purposes only. It should not be further distributed, disclosed,
copied or otherwise furnished to any other party without Milliman’s prior consent.
No reliance should be placed on the results or graphs presented herein and no inference made about theappropriateness of the different bases presented. In particular, independent verification and professional advice should
be sought when establishing company bases and assumptions for the purposes of pricing, valuation and transaction
purposes etc.
Actual experience may be more or less favourable than the assumptions and illustrations presented in thispresentation. To the extent actual experience differs from these, so will actual results differ from those presented.

62© 2014 Milliman

Questions?

Joshua Corrigan
joshua.corrigan@milliman.com

Visit milliman.com