roktokorobii by Rabindra Nath Tagoresomen/RNatok/ORX/roktokorobi_orx.pdfp kA
Simon Willis - OrX
Transcript of Simon Willis - OrX
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Taking value from
sharing data
4 June 2009
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ORX is a not-for-profit industry association headquartered in Zurich, Switzerland
ORX was founded primarily as a platform for securely sharing high quality operational risk
loss data
This is still what we do but ORX also works with its members to:
develop operational risk management practice set common standards for the industry develop professional networks
conduct leading edge research
ORX now has 52 members from 18 different countries
The ORX Global Banking Database now contains approx. 124,000 loss events to a value of
approx. 40 Billion
What is ORX?
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Banks have always managed operational risk
Operational risk management began in 1999 (maybe)
What have we spent the last 10 years doing and what have we achieved:
Better definitions
Better data
Better tools
Better measures and models
Better management
How as a discipline have we added value to our industry and our firms?
How as a discipline will we add value to our industry and our firms?
What is operational risk management for?
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Operational Risk Evolution
Op Risk first discussed asidentifiable risk class
Basel II initiative begins
Early movers createORM function
Industry working groupsformed IIF, ISDA, ITWG
Governing principlesestablished
Vision created to betterunderstand op risk
1999 -
2001
The Aspirational Years
Regulatory rules finalized
AMA qualification process
begins
Complexity of challengebecomes real
Firms struggle with costs,implementation and value
Value-added analytics
Efforts overshadowed byfinancial crisis
2005 -
2008
Pursuit of Value Begins
More banks form ORM function
Implementation begins
Basel II gains momentum
QIS provides insights
Sarbanes Oxley + / -
Significant losses incurred
ORX formed
Compliance vs. riskmanagement debate emerges
Risk quantification begins
2002 -
2004
The Development Years
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Risk management and risk measurement are fundamental activities
Risk models are only as good as the decisions that get made based upon them
Models only answer questions, dont ask them
Risk managers need to:
Think more broadly and challenge assumptions
Look at the specific but also understand the links and interdependencies
Learn from the past but know that the future can be very different
Recognise the importance of communication
Operational risk needs to think about its own role and mandate
Continuing value in improved risk measurement, opportunity for improved risk management
What might change in the future?
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Is there a role for external data
Fundamental value of external loss data is that it offers a larger sample than your own
experience
To make no mistakes is not in the power of man; but from the errors and mistakes ofothers the wise and the good learn wisdom for the future
Plutarch
Banks can and do incorporate external loss data
To supplement internal data in quantitative models
Inform scenario analysis
Benchmark performance relative to peers
Validate the adequacy of internal data and capital
ORX now looking at risk data not just loss data
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Loss Data Has Been a Catalyst of Change
Data collected sporadically, if at all
No requirements or standard
practices
No loss profile or time series ofdata
Anecdotal reporting
Culture of blame
Limited transparency andawareness
Limited engagement of businessexecutives in control environment
No mechanism for data sharing
Before
Firm-wide loss data collection
Standard definitions and
recording standards established
Time series of data developing
Incorporated in risk reporting and
business MIS
Culture shift to risk management
Greater transparency, escalationand accountability
Significant attention by regulatorycommunity
After
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Loss Data Enables a More Analytic Approach
ExternalLoss Data
(ORX)
InternalLoss Data
BaseCapital
StatisticalModel
Risk-basedCapital
QualitativeAdjustment
1. CALCULATE BASE CAPITAL 3. ASSESS CAPITAL
APPROPRIATENESS
2. QUALITATIVE
ADJUSTMENT
Internal Losses
Exter nal Losses
Int ernal and
External
Benchmarks
Data helps eliminates the challenges of subjectivity, repeatability and statisticalincorporation of results
Scenario
Analysis
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Overall Summary of ORX Annual Data
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ORX Global Membership (May 2009)
ABN Amro
Banco Bilbao VizcayaArgentaria
Banco Pastor
Banco Popular
Banco Portugus de Negcios,Banc Sabadell
Bank Austria Creditanstalt
Bank of America
Bank of Ireland
Bank of Nova Scotia
Barclays Bank
BMO Financial Group
BNP Paribas
Bradesco
Caja Laboral
Cajamar
Caixa Catalunya
Lloyds TSB Bank plc
National Australia Bank
Northern Trust
PNC
Postbank
Rabobank
Royal Bank of Canada
Royal Bank of Scotland
Santander
Skandinaviska EnskildaBanken
Standard Chartered
State Street
TD Bank Financial Group
US Bancorp
Wachovia Corporation
Wells Fargo
WestLB
Caixanova
Capital One
Commerzbank AG
Credit Agricole
Danske Bank A/S
Deutsche Bank AG
Dresdner Bank AG
Erste Bank
Euroclear Bank
First RandFortis
Grupo Banesto
Hana Bank
HSBCHBOS plc
ING
Intesa SanPaolo
JPMorgan Chase
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Total Number and Value of Losses by Year
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Total Number and Value of Losses by Last 6 Quarters
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Loss Events Frequency (2002-2008)
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Internal
Fraud
External
Fraud
Employment
Practices &
Workplace
Safety
Clients,
Products &
Business
Practices
Disasters &
Public Safety
Technology
&
Infrastructure
Failures
Execution,
Delivery &
Process
Management
Malicious
Damage Total
% of
Total
Corporate Finance 21 112 141 308 1 5 330 0 918 0.74%
Trading & Sales 95 268 401 645 18 670 11,091 0 13,188 10.64%
Retail Banking 4,153 39,725 8,101 7,822 844 1,201 16,279 194 78,319 63.16%
Commercial B anking 185 4,207 393 1,669 59 261 4,440 1 11,215 9.04%
Clearing 52 530 123 105 3 156 1,768 0 2,737 2.21%
Agency Services 16 55 96 159 5 61 2,412 0 2,804 2.26%
Asset Management 52 110 141 586 10 76 2,206 1 3,182 2.57%
Retail Brokerage 209 161 515 1,856 10 55 1,222 1 4,029 3.25%
Private Banking 152 414 165 1,541 25 66 2,651 2 5,016 4.05%
Corporate Items 34 301 667 315 215 76 974 10 2,592 2.09%
Total 4,969 45,883 10,743 15,006 1,190 2,627 43,373 209 124,000
% of Total 4.01% 37.00% 8.66% 12.10% 0.96% 2.12% 34.98% 0.17%
1%5% 5%10% >10%
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Gross Loss / 100 Gross Income 2002-2008
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Gross Loss / 100 Gross Income by Last 6 Quarters
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Business Line Ranking
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The challenges of using external loss data
Fundamental challenge of external loss data is that it is not your own experience
Key challenges when using external loss data include:
Banks are different: size, location, business mix, control environment
Banks collect data differently: categorisation, truncation point, currency
Confidentiality limits information data is anonymous, detail is limited
Important however not to overstate the differences between banks
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Loss data homogeneity
What similarities exist in the size and shape of the loss distributions from Members
Similarity is measured in terms of:
Statistical measures of goodness-of-fit among loss distributions
Reduction of error in predicting large losses as a result of using pooled data rather thaninternal data alone
Overall ORX data showed the following results:
A high level of homogeneity was evident in the shapes of various loss distributions
across all levels in the sample
Simple scaling relations were effective in aligning many loss distributions
Pooling losses among banks with similar loss distributions can result in (estimated at
20-30%) error reductions when estimating high quantiles of the loss severity distribution
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Dealing with heterogeneity among data sources
How can we compare losses across ORX banks?
If the Bank of America reports a $1 million loss in External Fraud / Retail Banking,does that mean that the Bank Austria faces the same probability of such a loss?
The value of consortium data increases with banks ability to translate otherslosses into their own
Solution: development of loss scaling models
We determine the degree of similarity among distributions of various categories of lossesand adjust for differences in business line, location ,size of bank and size of loss:
Same distribution pool the data
Same distribution after applying a simple scaling relation scale the data, thenpool
Different distributions do not pool the data, build separate loss models
We have developed loss severity models for each loss category based on scaled and
pooled data
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Corporate Finance Internal Fraud
Sample Analysis
Differences in loss scale were often evident across regions
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Example: Scaling helps compare losses from
different regions
Distributions of (log)-losses occurring in North America and Western Europe for Private
Banking losses in Clients, Products and Business Practices. Applying a simple scale
factor to North American losses brings the two distributions into close alignment.
(Taken from Cope, Eric, and Simon Wills. External Loss Data Helps: Evidence from the ORX Database.
OpRisk &
Compliance. March 1, 2008.)
4 5 6 7 8
0.0
0.2
0.4
0.6
0.8
1.0
Clients, Products , and Business Practices / Private Banking
Log LOSS (Euro)
EmpiricalCDF
North AmericaWestern Europe
4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5
0.0
0.2
0.4
0.6
0.8
1.0
Clients, Products , and Business Practices / Private Banking
Log LOSS (Euro)
Emp
iricalCDF,
ScaledData
North America (Scaled)Western Europe
Clients, Products, and Business Practices / Private Banking
Log Loss (Euro) Log Loss (Euro)
Cumula
tiveProbability
Cumula
tiveProbability
Raw Losses
North America
Western Europe
Scaled Losses
North America
Western Europe
LowSeverity
HighSeverity
LowSeverity
HighSeverity
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Observed correlations across loss categories are
low Observed correlations of total quarterly losses across loss categories are small
Avg correlation for business line pairs: 6.6% (st dev 18.3%)
Avg correlation for event type pairs: 5.8% (st dev 18.5%)
Avg correlation for business line / event type combination pairs: 5.9% (stdev 17.2%)
Over 80% of banks correlation matrices are not distinguishable from consortiumaverage
Most individual banks may use the consortium correlation matrix in place of theirown
Histogram of Kendall's Tau Severity Corrs
Correlation Value
Frequ
ency
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6
0
200
40
0
600
800
Curve shows a normaldistribution with matching
mean and variance
Standard deviation is
within theoretical expected
range, based on amount of
available quarterly data
values
Histogram of Correlations among Quarterly Total
Losses by Business Line / Event Type pair
Kendalls Tau
Frequency
.06
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ORX is taking these building blocks and lessons learnt and building out our capacity to
measure risk across our membership
Increasing value from ORX data
Empirical quantiles
%pointsoffitteddistribution
20,000 50,000 200,000 500,000 2,000,000 10,000,000
10
50
90
99
99.9
99.97
99.99
99.999
IF1
1) Fit Severity Distributions
9.0 9.2 9.4 9.6 9.8 10.0 10.2
5
6
7
8
9
EL 4: Lo g TOTAL INCOME: After QM
Log TOTAL INCOME
LogLosses
2) Apply Scaling Models
Num Losses
Density
5 10 15 20
0.00
0.05
0.10
0.15
3) Fit Frequency Models
5) Apply Correlation Models
4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5
0.0
0.2
0.4
0.6
0.8
1.0
IF1
x
Fn(x)
6) Estimate annual totalbank losses
4) Compute aggregate lossdistributions
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More progress can be made together
Industry-wide benchmarking
Critical event analysis
Business unit benchmarking
Trend analysis
Correlation with KRIs
Correlation with environment
Dynamic reporting
Industry-wide risk measure
Business unit / risk type measure
Op risk correlation analysis
Peer group / homogeneity analysis
Use of Scaling analysis
Forward looking measures
Cross risk-class correlation
Risk Measurement Risk Management
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Our objective is to identify differences between firms and, at a granular level, identify andmeasure those factors that are driving the difference
What happened, to who, how much and why
Loss severity drivers such as jurisdiction, type of counterparty or claimant, role of the firm
ORX is supporting the creation of sub-sets of data and sub-sets of members
Looking to:
Improve ability to select relevant event data
Support improved peer group benchmarking
Support improved scenario analysis
Facilitate better discussion with the business
ORX Risk Management Tools
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Active review of the data requirement s for the Global Database
Product and Process
Require reporting per loss by end 2010
Exposure Indicators
Considering plans to expand / vary the Exposure Indicator data collected by Business Line
Large Loss Events
Work has begun on a review of data requirements for Large Loss Events (>10 million)
Seeking to establish process for the common categorisation of Large Loss Events
Developing Data Requirements
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Large Loss Event Template - Illustration
Type:
Auction Rate Securities
Context: Bonds were issued where the coupon was periodically re-set based upon theyields set by the auction of reference instruments. At the time of issuance the reference
instruments auctions were heavily over-subscribed, but soon after were under-subscribed
raising concerns about yields and market liquidity.
Description: Investors claimed that conditions had materially changed and the basis upon
which they bought the bonds no longer prevailed. An unanticipated quality option, in theform of the market for the reference instruments, had materialised.
Resolution:
A number of issuing banks agreed to buy-back the issued bonds at par at the
next auction of the reference instruments.
Recordable Loss Amount:
Regulatory Fine
Categorisations:
Operational Risk Event, Operational Risk Loss
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Business Line Corpor at e Finance/Corpor at e Finance
Event Type Cli ent s, Pr oduct s,. . /Pr oduct Flaw
Product Capi t al Rai si ng /Bond Issuance
Process Market Pr oduct s &
Services
Causes: Assumpt i on aboutst at us quo
Control Type Preventat ive
Control Failure Comp let eness of
Documentat ion
Scaling Vol ume of New
Products
Loss Severity Driver Tot al i ssuance si ze Business Environment Legal , Social
Claimant Type Professional
Invest ors
Impact Balance Sheet
Growth
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ORX is seeking to establish National and Sector database from within current
membership and as a service to future members
Working to establish a Canadian national service, with 11 members to establish an ORX
Insurance Sector Service, with 8 members to establish Investment Banking Service and
soon will invite membership of a Global Custody Sector Service and Global Fraud Service
National and Sector Services use the ORX legal, security and system platform but have
the capacity to:
Define own loss data categorisation and standards including: loss attributes;
text fields; business metrics and KRIs
Define own frequency of loss submission and distribution
Set own quality assurance testing and reporting regime
Create own reports and benchmarking
Use ORX global analytical tools and routines
ORX objective is to develop bespoke National and Sector services as business level tools
Creating new trend and comparative data directly relevant to business units and directly in
support of business decisions
Developing risk management tools
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ORX has operated the Spanish National Service since 1 January 2007 on behalf of 10members:
Banco Bilbao Vizcaya Argentaria
Banco Pastor
Banco Popular
Banc Sabadell
Barclays Bank
Caja Laboral
Cajamar
Caixa Catalunya
Grupo Banesto
Grupo Santander
Spanish National Service has own governance determining who can participate, settingquality standards, monitoring quality standards and setting data requirements
Collected approximately 28,000 loss events
Participation in the Spanish National Service charged at 5,000 per annum
Spanish National Service
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ORX is working with 11 members to establish an ORX Insurance Sector Service
The Service will be strongly based on the Global ORX Database National but vary in terms
of:
Retain ORRS base loss data reporting standards and format
Business Lines add 4 new insurance business lines
Products add approx. 16 new insurance product types
Exposure indicators define 2 additional exposure indicators
Retain quality assurance testing and reporting regime varying only forchanges made
Retain reports and benchmarking varying only for changes made
Retain standard timetable and data cycle
Hope to launch invitation plus specification before end 2008
Sector Service Example: Insurance
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Summary
We have made a great deal of progress in the last 10 years
The current crisis is an challenge and an opportunity
We need to continue to invest and improve risk measurement
We need to add more value as risk managers
Sharing data can help us move forward
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