Operational Risk Management 2.0 - Reconnecting risk and control framework

Operational Risk Management
April 2016
SMA – Study on the effects of the new methodology recommended by the
Basel Committee
Benoît Genest – bgenest@chappuishalder.com
Hélène Fréon – hfreon@chappuishalder.com

Operational Risk Management 2.0 – Reconnecting risk and control framework
An overview of the SMA review
SUMMARY | Operational Risk Capital Requirement (ORCR)
As per the consultative document on the Review of Op Risk Measurement Approach (March, 2016)
In March 2016, Basel committee released a second
consultative paper outlining the new Standardised
Measurement Approach (SMA), aiming at replacing
the 3 existing approaches – including the AMA.
𝑺𝑴𝑨 𝑪𝒂𝒑𝒊𝒕𝒂𝒍 𝑹𝒆𝒒𝒖𝒊𝒓𝒆𝒎𝒆𝒏𝒕 = 110 𝑀 + ( −110 𝑀) ∙ 𝑙𝑛 exp 1 − 1 +A
A
B
The BI component reflects the Op loss
exposure of an average bank of a given BI
Size.
5 buckets have been defined according to
the size of Bank’s BI :
— Bucket 1 : BI = [0 ; 1bn€[
— Bucket 2 : BI = [1bn€ ; 3bn€[
— Bucket 3 : BI = [3bn€ ; 10bn€[
— Bucket 4 : BI = [10bn€ ; 30bn€[
— Bucket 5 : BI = [30bn € ; +∞[
Business indicator Component (BIC)A
BI Component = 0,11 ∙ 𝐵𝐼1
110 𝑀€ ∙ 0,15 𝐵𝐼2 − 1𝑏𝑛€
410 𝑀€ ∙ 0,19 𝐵𝐼3 − 3𝑏𝑛€
1,74 𝑏𝑛€ ∙ 0,23 𝐵𝐼4 − 10𝑏𝑛€
16,34 𝑏𝑛€ ∙ 0,15 𝐵𝐼5 − 30𝑏𝑛€
Loss Component (LC)B
The loss Component reflects the Op Loss
exposure inferred from Bank internal loss
experience over the past 10 years.
3 Loss Classes LCi are set ; a weighting
coefficient i is associated to the average of the
events in each LCi
— LC1: any loss event | 1 = 7
— LC2: Loss events > 10 M€ | 2 = 7
— LC3: Loss events > 100 M€ | 3 = 5
Loss Component = 7 ∙ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑡𝑜𝑡𝑎𝑙 𝑙𝑜𝑠𝑠𝑒𝑠
+ 7 ∙ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 total losses > 10 M€
+ 5 ∙ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 total losses > 100 M€
Standardised Measurement Approach (SMA), 2 components…
… « One fits all » formula
Key objectives of BCBS SMA
 Standardization | Overriding objective remains to
improve the resilience of the global banking system,
promote confidence in regulatory capital ratios and
encourage a level playing field for Op Risk across banks
(something AMA failed to achieve, according to the
Basel Committee)
 Comparability & transparency | Furthermore, the
inclusion of a single non-model-based method for the
estimation of Op Risk capital will ensure fairer
comparison across banks and more transparency in the
estimation Op risk capital and RWA
 Risk-sensitivity | The approach should also incorporate
the risk sensitivity of an advanced approach, using for
each bank the combination of the items of its financial
statement (BI Component) and internal loss experience
(Loss Component).

Agenda
2
3
4
1
5 Appendix

With the new methodology submitted by BIS,
capital charges are linearly dependant of the
bank Net Income.
Inclusion of changes depending on the BI but
also internally :
 SMA Op Risk Capital Requirements display more or
less significant contrasts if the ratio LC/BIC is
different from 100%: the lower the ratio the deeper
the gap
 Capital requirements are not linear with the BI since
SMA capital grows more rapidly than the largest
buckets
 The model does not take into account the frequency
of the losses
 A heavy loss can be accounted for 10 years
SMA – Sensitivity analysis
Overview of the calculation of Op Risk capital requirements
SMA
Reference Simulation
CALCULATION OF CAPITAL REQUIREMENTS | Potential variations
Function of the BI and internal loss
-
2 000
4 000
6 000
8 000
10 000
12 000
14 000
0
2400
4800
7200
9600
12000
14400
16800
19200
21600
24000
26400
28800
31200
33600
36000
38400
40800
43200
45600
48000
OPRISKCAPITAL
REQUIREMENT(INMEUR)
BUSINESS INDICATORS (IN M EUR)
Loss Component < BI component Loss Component > BI component
-
2 000
4 000
6 000
8 000
10 000
12 000
14 000
0
3900
7800
11700
15600
19500
23400
27300
31200
35100
39000
42900
46800
Bucket1
Bucket2
Bucket3
Bucket4
Bucket5 -
2 000
4 000
6 000
8 000
10 000
12 000
14 000
0
3900
7800
11700
15600
19500
23400
27300
31200
35100
39000
42900
46800
Bucket1
Bucket2
Bucket3
Bucket4
Bucket5
No consideration
of internal loss
effects:
Ratio = 100%
Isolation of the BI
effect only
1 2
Ratio = 25% Ratio = 250%
Consideration of internal losses
effects
2 tested options

Presentation of 4 potential loss profiles (theoretical simulation)
In order to illustrate the sensitivity of SMA methodology to the internal loss profile of a bank, four loss profiles with different distributions
are simulated (theoretical and “exaggerated” cases to emphasize the effects and reproducibility hypothesis of the profile on 10 years)
Amountofaggregatedloss(inMEUR)
898
10 M 100 M0 M
• Simulation of a loss profile
following a leptokurtic
distribution : the strong financial
impacts show low probabilities
contrary to low and more
frequent risks
• Statistics of the used loss sample:
• 2,000 losses < 10 M EUR
• Average = 0.449 MEUR
• Stand. Dev. = 388 K EUR
10 M 100 M0 M
655
• Distortion of case 1 with a loss
profile focused on amounts
lower than 10M EUR
• 38 losses > 10 M EUR
• Average = 0.744 M EUR
• Stand. Dev. = 2.350 M EUR
832
896
10 M 100 M0 M
2 583
• Profile similar to case 1 with
one unique heavy loss (more
than 2,583 M EUR) on the whole
profile
• 1 loss> 100 M EUR
• Average= 1.740 M EUR
CASE 1 | Lack of a distribution tail CASE 2 | Lack of extreme losses (distortion of case 1)
CASE 3 | Presence of a fat tail
-12%
832
10 M 100 M0 M
540
• Values of loss mainly lower
than 10M EUR on the 2,000
simulated observations
• Average = 1.260 M EUR
1 149
CASE 4 | Mix of case 2 and 3

Capital requirement calculation with the SMA methodology
• 10M < SMA Capital
< 6,650 M EUR for
a BI interval equal
to [100 M EUR ;
50,000 M EUR]
• The difference with
a 100% ratio is
more important for
large buckets (LC =
3.14 M EUR)
Bucket1
Bucket2
Bucket3
Bucket4
Bucket5
0.2%<LC/BIC<29% 0.05%<LC/BIC < 0.2% LC/BIC < 0.05%
< 6,700 M EUR
• The ratio decreases
from 1,100% to
10% between the
buckets 2 and for
LC = 126 M EUR
Bucket1
Bucket2
Bucket3
Bucket4
Bucket5
7.3%<LC/BIC 2%<LC/BIC< 7.3% LC/BIC < 2%
< 12,500 M EUR
• The ratio is high
(LC=12,930MEUR)
for the lower
buckets, worsening
the gaps. It
decreases and
comes up to 100%
for the last bucket
Bucket1
Bucket2
Bucket3
Bucket4
Bucket5
743%<LC/BIC 743%<LC/BIC < 204% 106.5%<LC/BIC < 743%
CASE 1 | Results in terms of capital charges CASE 2 | Results in terms of capital charges
CASE 3 | Results in terms of capital charges
-79.9%
-82.8% -80.2%
-73.9%
+37.3%
+59%
+12.3%
BI (in M EUR) BI (in M EUR)
BI (in M EUR)
< 7,340 M EUR
• For the buckets 2
and 3, the ratio is
greater than 100%
due to a high LC
(1,272 M EUR)
• The ration comes
back to usual ratios
for the following
buckets
Bucket1
Bucket2
Bucket3
Bucket4
Bucket5
74%<LC/BIC 20%<LC/BIC < 74% 1.0%<LC/BIC < 20%
CASE 4 | Results in terms of capital charges
-36.9%
-60%
BI (in M EUR)

Agenda
2
3
4
1
5 Appendix

SMA – Market comments overview
Many comments tend to revolve around 5 themes
SURVEY | What do specialists in Operational Risk think
More than twenty recurring remarks
Many responses since the publication in
March 2016
The key points are the following:
- A regress which jeopardize banks’ efforts on
detection and measurement of their operational
risks
- Methodological simplifications which question
the relevance of the submitted model
- A growing antagonism between the regulators
positions and those supported by the market
However, the initial objectives of the regulator
are reached nonetheless:
- Increase of capital charges relative to
operational risk
- A convergence in methodology and
therefore results (tool for benchmarking)
- A relative sensitivity to the bank’s loss
profile
21
4
3
8
3
3
ManagementGovernanceTotal Measure Impacts
/ Costs
Strategy
Governance
1.Op Risk models are
useful to the
banking industry
2.Methodology
insensitive to
decisions (Change
of Business model)
3.Loss of interest for
the Op Risk
governance
4.Less incentive to
improve Op Risk
management (IL
management)
Management
1.SMA : source of Op
Risk by itself
2.More control of
inherent and
residual risks
3.Expected impacts
on the quality of
monitoring
(Reporting & Data
Quality)
Impacts / Costs
1.No return on
investment
2.Increase of capital
charges
3.Instability of
capital charges
(possible jump
effects)
Measure
1.Not forward
looking
2.Risk of over fitting
(calibration on QIS
2015)
3.Calibration not
auditable (no
explanations)
4.End of the diversity
on loss data
5.No answers on
already known
limits of AMA
6.Underestimation of
the idiosyncratic
risk
7.Simplistic
methodology
8.Expected impacts
on economical
capital
Strategy
1.More complexity
on risk transfer
2.Cross-Risk
3.Less contributions
of industry
specialists
(consulting,
Software vendors,
Quants…)

Details of the main comments (1/3)
Argument #1 : A more risky than it seems arbitrage
Discarding the AMA and replacing it with the SMA could very well become a source of operational risk
in itself. (Risk.net)
Argument #2 : Expected consequences concerning the deterioration of the management quality
One of the risks with this approach is the damaging consequence on event reporting and classification
Argument #3 : More management of inherent and residual risks
…/… Those who've used AMA have added good risk management programmes that have contributed to
lowering the inherent and residual operational risks. (Northern Trust)
Argument #1 : Less incentive to improve Op Risk management
It’s not only the AMA that is potentially being dropped, but also multiple risk management benefits that
have come from the implementation of the framework
Argument #2 : A strong risk of lack of interest for the governance of Op Risk
"For smaller banks, once they've seen the statements from the Basel Committee, they may think it's
not worth it to invest in internal models for op risk," says the US-based policy expert.
"For the large banks who already appreciate the importance of having internal measures of operational
risk, the danger is it might result in the perception that op risk is not as important as credit and market
risk and of course it will have an impact on the resources that will be made available.“
Argument #3 : A methodology insensitive to the efforts/decisions to improve Op Risk management
The only one of these reflected in the SMA is internal losses, meaning that any improvements made to
risk controls or by changing the firm's business model won't be reflected in the capital charge
Argument #4 : The models are (were) useful to the industry
Models will continue to play an important role in quantifying risk and should support sound operational
risk management," said Beth Dugan, deputy comptroller for operational risk at the US Office of the
Comptroller of the Currency (OCC)
“I want to make it perfectly clear that we intend to continue to promote the need for all of our banks to
practice sound operational risk management, including enhancement of modelling and other
measurement techniques," said Dugan.
Governance | 4 key comments
A risk of lack of interest for bank management
Operational Risk management | 3 key comments
Worries on deterioration of risk management
Main Source : Risk.net

Argument #1 : No explanation enables any justification / audit for the calibration submitted by SMA
The proposed approach is supported by no evidence and no mention is made of potential recurring
calibrations.
Argument #2 : SMA does not answer to the already known limits of the AMA
Others claim the SMA fails to fix some of the shortcomings of the AMA. “The problems that we’re
seeing in AMA have been recognised from the beginning,” said Cope of Credit Suisse. “The 99.9
standard was fundamentally unattainable, which led to a disconnect between risk measurement and
risk management. Is SMA addressing either of those problems? I would say it isn't.”
Argument #3 : SMA is not forward-looking (no prospective approach on future loss)
"For me, [the proposal is] backwards looking. I'm not sure if it's protecting the banks from future
potential losses, and that's quite an issue because I don't think it's really fit for purpose," says Bertrand
Hassani, group head of non-financial risk methodologies at Santander
Argument #4 : An approach that tends to underestimate the idiosyncratic nature of Op Risk
The importance of factors such as corporate culture and geography is neglected by simpler
approaches that rely on proxies such as gross income or the business indicator, they argue. "The
original authors of AMA seemed to understand the inherent nature of operational risk when they
wrote AMA – namely, that operational risk is largely idiosyncratic to an institution," says Northern
Trust's Rosenthal.
Argument #5 : A too simplistic approach to be relevant
A one-size-fits-all formula is not relevant for operational risk (Société Générale)
Argument #6 : Heavy impacts expected in the modelling of Economic Capital
"What will be impacted for sure will be the part of business that is specifically related to building
economic capital models," notes Renzo Traversini, head of the European and Asia-Pacific risk
management team at software vendor SAS.
Argument #7 : A strong risk of over-fitting (requirements too conservative)
The proposed approach seems to be calibrated on the last QIS.
Argument #8 : The review of the approach marks the end of the diversity on loss data
Solutions originally discussed during the reform of the AMA regime included more rigorous scenario
analysis; the increased role of BEICFs; and stricter loss distribution approach rules. Any of those would
be far more constructive approaches.
The AMA had this diversity of perspectives associated with it. You had the scenarios, you had BEICFs,
you had the external data; the industry perspective. That’s something that’s quite explicitly missing
from the SMA. (Credit Suisse)
Measure of Operational Risk | 8 key comments
A regress and trouble causing simplifications

Argument #1 : No ROI… despite the heavy investments of the last years
During the past decade, major banks have invested heavy sums in the personnel and IT infrastructure
needed to undertake op risk modelling. With little or no incentive to continue this work, banks fear
such investment could be lost.
Argument #2 : An approach which does not guarantee the stability of capital charges (possible jump
effects)
Meanwhile, the 10-year cut-off could create a 'cliff effect', with capital numbers dropping dramatically
as large losses hit the 11-year mark. it will definitely not resolve the problem of capital stability over
time.
Argument #3 : An increase to be predicted in capital requirements
Most institutions were opposed to the RSA because it would likely have resulted in an increase in
operational risk regulatory capital.
Argument #1 : Which future for Op Risk if specialists lose interest?
Then there's the multitude of op risk modelling specialists, consultants, loss databases and software
vendors that have developed to help banks implement their AMA models. Such companies may have
a harder time attracting business in future, say industry observers
Argument #2 : Cross-risk which are not to be neglected
There is a risk that we will observe cross-risk arbitrage – for example, events such as collateral failure
being booked in credit loss categories to avoid their inclusion in the op risk capital charge.
Argument #3 : A dead end to manage the transfer of the risk, leaving little space for options
“My biggest fear of what will happen with SMA is that the knowledge and the work that we’ve done
will not be allowed to transfer risk” (Reserve Bank of Chicago)
Impacts & costs | 3 key comments
An investment at a loss for a more expensive methodology
Strategy of Operational Risk | 3 key comments
A leak of R&D and less flexibility

Agenda
2
3
4
1
5 Appendix

SMA – Predicting what is at stake
What lesson for the banks?
CONCLUSION| For discussion POSSIBLE ISSUES | Different timings
For reflexion and discussion
Short Term
Middle Term
Next steps are quite simple to think of, whether they are direct
(increase in capital charges, opportunity costs on implantation of
AMA models, etc.) or indirect (investments in operational risk,
reporting requirements, etc)
This discussion raise the following questions:
• How to keep on mobilising the efforts on operational risk at
every level in the bank?
• Is this what we want? And under what form?
• How to make investment in the establishment of operational
risk profitable?
• Which monitoring and surveillance setup do we want to give:
 A more quantitative approach? Less quantitative?
More centered on operationals’ performance?
 How to draw a ROI on this risk?
 Etc.
• Internal loss data being now a key element of the reform, how
to make the most of the source of information and its quality?
⁻ Lobbying? Defend the banks’ interests (e.g. via local organisations) in preparation for
the formulation of the final text
⁻ Benchmarking? Perform an overview of concrete actions, taken or anticipated by others
⁻ Impact? Simulate the effects of the change of the methodology on capital charges
⁻ Organisation? Anticipate the effects of the future reform on the current organisation
⁻ Communication? Manage the “image” effect of such a reform. Train operationals to
expected changes
⁻ Budgetary planning? Diagnose early impacts on direct support to the AMA method
(software, DWH, J/H, consulting) and foresee the coming-down mode
⁻ Monitoring / Reporting? Rebuild or update dashboards or reports depending on the
reform (if necessary)
⁻ Strategy? Precise the role of operational risk in banks and its importance in risk
governance
⁻ Data Loss? This point is becoming a priority – especially from the regulator’s point of
view – the management and optimisation of loss/incidents gathering will become major
(if not already the case)
⁻ Establishment of a pattern? Think of a way to integrate Op Risk models with all the R&D
developed in banks
⁻ ROI? Develop a culture of concrete evaluation of the control and surveillance efforts on
operational risk. Validate and test the return on investment of the bank on this risk
⁻ Risk transfer? Develop new strategies for the hedging of major and transferable risks
⁻ Capital? Base the new strategy of capital consumption and repartition scheme of this
charge within bank’s entities

Agenda
2
3
4
1
5 Appendix

Appendix 1 – Analysis of the alternative methodology
0,00
1,00
2,00
3,00
4,00
5,00
6,00
0
7
14
21
28
35
42
49
56
63
70
77
84
91
98
105
112
119
126
133
140
147
154
161
168
175
182
189
196
InternalLossMultiplier
LC/BIC
Alternative : m = 3
Alternative : m = 4
Alternative : m = 5
Logarithmic
Method
• The regulator suggests an alternative to the
logarithmic calculation of the Internal Loss
Multiplier :
ILM =
𝑚𝐿𝐶+ 𝑚−1 𝐵𝐼𝐶
𝐿𝐶+ 2𝑚−2 𝐵𝐼𝐶
With m, factor to calibrate
• The submitted methodology avoids ILM
divergence: for different values of m, the loss
multiplier grows more slowly than the ILM
generated by logarithm and tend to converge
towards m
• Capital requirements calculated with the
alternative methodology are higher for the first
buckets of case 4. Ad infinitum, this methodology
presents inconclusive results due to the
complexity of calibrating the m factor, since
values converge towards equivalent levels
whatever the used methodology is
0
2000
4000
6000
8000
10000
12000
100
1800
3500
5200
6900
8600
10300
12000
13700
15400
17100
18800
20500
22200
23900
25600
27300
29000
30700
32400
34100
35800
37500
39200
40900
42600
44300
46000
47700
49400
OpRiskCapitalrequirement(enMEUR)
LC/BIC Case 4

Appendix 2 - Description of the sample
0
0,05
0,1
0,15
0,2
0,25
0 500000 1000000 1500000 2000000 2500000 3000000
Frequency
Losses
Histogram (Losses)
-
500 000
1 000 000
1 500 000
2 000 000
2 500 000
3 000 000
1
57
113
169
225
281
337
393
449
505
561
617
673
729
785
841
897
953
1009
1065
1121
1177
1233
1289
1345
1401
1457
1513
1569
1625
1681
1737
1793
1849
1905
1961
Observed Losses (1 year horizon)
Statistiques descriptives annuelles:
Variable ObservationsObs. avec données manquantesObs. sans données manquantesMinimum Maximum Moyenne Ecart-type
Losses 2 000 # 2 000 451 2 583 406 449 057 387 789
Statistiques descriptives pour les intervalles :
Borne inférieure Borne supérieure Effectif Fréquence Densité
- 130 000 429 21,5% 0,000
130 000 260 000 402 20,1% 0,000
260 000 390 000 288 14,4% 0,000
390 000 520 000 205 10,3% 0,000
520 000 650 000 143 7,2% 0,000
650 000 780 000 153 7,7% 0,000
780 000 910 000 120 6,0% 0,000
910 000 1 040 000 88 4,4% 0,000
1 040 000 1 170 000 57 2,9% 0,000
1 170 000 1 300 000 35 1,8% 0,000
1 300 000 1 430 000 33 1,7% 0,000
1 430 000 1 560 000 23 1,2% 0,000
1 560 000 1 690 000 9 0,5% 0,000
1 690 000 1 820 000 5 0,3% 0,000
1 820 000 1 950 000 5 0,3% 0,000
1 950 000 2 080 000 1 0,1% 0,000
2 080 000 2 210 000 0 0,0% 0,000
2 210 000 2 340 000 2 0,1% 0,000
2 340 000 2 470 000 1 0,1% 0,000
2 470 000 2 600 000 1 0,1% 0,000
Study sample
Distribution sample of operational losses used in this study:
• The sample was used for didactic ends only
• It was built on the generation of random loss yet following a pre-
defined distribution law

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