ch&co_sma review_op risk survey and challenges

Operational Risk Management

April 2016

SMA – Study on the effects of the new methodology recommended by the Basel Committee

Benoît Genest – [email protected]élène Fréon – [email protected]

2GRA – Op Risk | Survey | SMA © Chappuis Halder & Co.| 2016 | All rights reserved

Agenda

Overview of the SMA methodology

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Sensitivity analysis of the SMA methodology

What does the market think | Specialists opinions

Potential consequences

5 Appendix


Operational Risk Management 2.0 – Reconnecting risk and control frameworkAn 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 secondconsultative paper outlining the new StandardisedMeasurement Approach (SMA), aiming at replacingthe 3 existing approaches – including the AMA.

𝑺𝑴𝑨 𝑪𝒂𝒑𝒊𝒕𝒂𝒍 𝑹𝒆𝒒𝒖𝒊𝒓𝒆𝒎𝒆𝒏𝒕 = 110 𝑀 + ( −110 𝑀) ∙ 𝑙𝑛 exp 1 − 1 +AA

B

The BI component reflects the Op lossexposure of an average bank of a given BISize.

5 buckets have been defined according tothe 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 ∙ 𝐵𝐼1110 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 Lossexposure inferred from Bank internal lossexperience over the past 10 years.

3 Loss Classes LCi are set ; a weightingcoefficient i is associated to the average of theevents 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 toimprove the resilience of the global banking system,promote confidence in regulatory capital ratios andencourage a level playing field for Op Risk across banks(something AMA failed to achieve, according to theBasel Committee)

Comparability & transparency | Furthermore, theinclusion of a single non-model-based method for theestimation of Op Risk capital will ensure fairercomparison across banks and more transparency in theestimation Op risk capital and RWA

Risk-sensitivity | The approach should also incorporatethe risk sensitivity of an advanced approach, using foreach bank the combination of the items of its financialstatement (BI Component) and internal loss experience(Loss Component).


Agenda


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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 orless significant contrasts if the ratio LC/BIC isdifferent from 100%: the lower the ratio the deeperthe gap

Capital requirements are not linear with the BI sinceSMA capital grows more rapidly than the largestbuckets

The model does not take into account the frequencyof the losses

A heavy loss can be accounted for 10 years

SMA – Sensitivity analysisOverview of the calculation of Op Risk capital requirements

SMA Reference Simulation

CALCULATION OF CAPITAL REQUIREMENTS | Potential variationsFunction of the BI and internal loss

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Loss Component < BI component Loss Component > BI component

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No consideration of internal loss effects: Ratio = 100%

Isolation of the BI effect only

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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)

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10 M 100 M 0 M

• Simulation of a loss profilefollowing a leptokurticdistribution : the strong financialimpacts show low probabilitiescontrary to low and morefrequent risks

• Statistics of the used loss sample:• 2,000 losses < 10 M EUR• Average = 0.449 MEUR• Stand. Dev. = 388 K EUR

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• Distortion of case 1 with a lossprofile focused on amountslower than 10M EUR

• Statistics of the used loss sample:• 38 losses > 10 M EUR• 1,962 losses < 10 M EUR• Average = 0.744 M EUR• Stand. Dev. = 2.350 M EUR

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2 583• Profile similar to case 1 with

one unique heavy loss (morethan 2,583 M EUR) on the wholeprofile

• Statistics of the used loss sample:• 1,999 losses < 10 M EUR• 1 loss> 100 M EUR• Average= 1.740 M EUR• Stand. Dev. = 57.8 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%

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• Values of loss mainly lowerthan 10M EUR on the 2,000simulated observations

• Statistics of the used loss sample:• 1,962 losses < 10 M EUR• 33 losses > 10 M EUR• 5 losses > 100 M EUR• Average = 1.260 M EUR• Stand. Dev. = 11.7 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 fora BI interval equalto [100 M EUR ;50,000 M EUR]

• The difference witha 100% ratio ismore important forlarge buckets (LC =3.14 M EUR)

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0.2%<LC/BIC<29% 0.05%<LC/BIC < 0.2% LC/BIC < 0.05%

• 10M < SMA Capital< 6,700 M EUR

• The ratio decreasesfrom 1,100% to10% between thebuckets 2 and forLC = 126 M EUR

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7.3%<LC/BIC 2%<LC/BIC< 7.3% LC/BIC < 2%


• The ratio is high(LC=12,930MEUR)for the lowerbuckets, worseningthe gaps. Itdecreases andcomes up to 100%for the last bucket

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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)


• For the buckets 2and 3, the ratio isgreater than 100%due to a high LC(1,272 M EUR)

• The ration comesback to usual ratiosfor the followingbuckets

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CASE 4 | Results in terms of capital charges

-36.9%

-60%

BI (in M EUR)


Agenda


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5 Appendix


SMA – Market comments overviewMany comments tend to revolve around 5 themes

SURVEY | What do specialists in Operational Risk thinkMore than twenty recurring remarksMany responses since the publication in

March 2016

The key points are the following:

- A regress which jeopardize banks’ efforts ondetection and measurement of their operationalrisks

- Methodological simplifications which questionthe relevance of the submitted model

- A growing antagonism between the regulatorspositions and those supported by the market

However, the initial objectives of the regulatorare 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

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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-Risk3.Less contributions

of industry specialists (consulting, Software vendors, Quants…)


SMA – Market comments overviewDetails of the main comments (1/3)

Argument #1 : A more risky than it seems arbitrageDiscarding 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 managementIt’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 managementThe 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 industryModels 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 commentsA risk of lack of interest for bank management

Operational Risk management | 3 key commentsWorries on deterioration of risk management

Main Source : Risk.net



Argument #1 : No explanation enables any justification / audit for the calibration submitted by SMAThe 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 AMAOthers 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 RiskThe 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 relevantA 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 dataSolutions 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 commentsA regress and trouble causing simplifications




Argument #1 : No ROI… despite the heavy investments of the last yearsDuring 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 requirementsMost 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 neglectedThere 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 commentsAn investment at a loss for a more expensive methodology

Strategy of Operational Risk | 3 key commentsA leak of R&D and less flexibility



Agenda


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5 Appendix


SMA – Predicting what is at stakeWhat lesson for the banks?

CONCLUSION| For discussion POSSIBLE ISSUES | Different timingsFor 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 ofAMA 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 atevery level in the bank?

• Is this what we want? And under what form?

• How to make investment in the establishment of operationalrisk 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, howto make the most of the source of information and its quality?

⁻ Lobbying? Defend the banks’ interests (e.g. via local organisations) in preparation forthe 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 riskgovernance

⁻ Data Loss? This point is becoming a priority – especially from the regulator’s point ofview – 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&Ddeveloped in banks

⁻ ROI? Develop a culture of concrete evaluation of the control and surveillance efforts onoperational 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


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5 Appendix


Appendix 1 – Analysis of the alternative methodology

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Alternative : m = 3

Alternative : m = 4

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Method• The regulator suggests an alternative to the

logarithmic calculation of the Internal LossMultiplier :

ILM =𝑚𝐿𝐶+ 𝑚−1 𝐵𝐼𝐶

𝐿𝐶+ 2𝑚−2 𝐵𝐼𝐶

With m, factor to calibrate

• The submitted methodology avoids ILMdivergence: for different values of m, the lossmultiplier grows more slowly than the ILMgenerated by logarithm and tend to convergetowards m

• Capital requirements calculated with thealternative methodology are higher for the firstbuckets of case 4. Ad infinitum, this methodologypresents inconclusive results due to thecomplexity of calibrating the m factor, sincevalues converge towards equivalent levelswhatever the used methodology is

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Appendix 2 - Description of the sample

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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

ch&co_sma review_op risk survey and challenges

Economy & Finance