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From Second Wave Basel II to Basel IIIA Credit Risk Perspective
By David Samuels
Managing Director S&P Capital IQ
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Contents
Introduction .....................................................................................................................................................................
The Regulatory Timeline ...............................................................................................................................................
Watch Out for Basel II Second Wave ..........................................................................................................................Sidebar 1: Loss Given Default A key opportunity ...............................................................................................
Tactics to manage Basel II Second Wave efficiently ............................................................................................
Basel III Big PicturePressure on Profitability .......................................................................................................
Business Impact .............................................................................................................................................................
Credit Risk Appetites and Management Levers ......................................................................................................
Sidebar 2: Trends toward risk-adjusted remuneration .......................................................................................
Improving Credit Risk Modeling ..................................................................................................................................
Sidebar 3: Top issues when assessing approaches to credit risk modeling .................................................
Credibility is KeyValidating the Models .................................................................................................................
Ongoing Risk Monitoring and Surveillance ..............................................................................................................
Efficient credit platforms ..........................................................................................................................................
Rapid credit surveillance ...........................................................................................................................................
Framework for forward-looking risk-based decision making ..........................................................................
Conclusion: Turning Second-Wave Basel II and Basel III to Your Advantage..................................................
How S&P Capital IQ Can Help You ...............................................................................................................................
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IntroductionThe Basel II and Basel III roadmap is now clear in terms of the broad sweep of the regulations and the
regulators intentions in principle.
How the regulations will be interpreted and augmented by national regulators is much less clear,
particularly as this may depend in part on the trajectory of a highly uncertain world economy. Despite
this, executives need to work out the key takeaways for their business and their credit risk managemen
strategythe focus of this paper.
As far as Basel II is concerned, our experience indicates that banks still have significant unfinished
business in terms of filling in deliberate gaps and placeholders left behind in the rush for compliance.
More problematically, they need to replace some models put in place for Basel II that have since proved
substandard. We call this the second wave of Basel II.
With regard to Basel III, despite some uncertainties about the detailed application of some key
mechanisms such as the countercyclical capital buffer, we can be sure that the new regulations will:
Ratchet up capital and funding costs for most banks in the main 2013-19 phase-in period
Drive banks to better identify which businesses make money after risk and capital costs are taken in
account, work capital more efficiently, and create operating efficiencies
This suggests that second-wave Basel II and Basel III should be strongly connected in executives
minds. In effect, Basel III will leverage the impact of the second-wave Basel II effort to model risk more
accurately. It will also prolong that effort as banks confront increasing capital costs and a difficult
business environment.
While this will be challenging, it also presents an opportunity to re-forge the connection between bank
risk-taking, bank capital, bank soundness and bank profitability.
In particular, banks need to turn their risk appetite setting programs into business reality using key
management levers such as improved capital allocation, performance measurement and remuneration
Most fundamentally, banks must strengthen and make credible their approaches to credit risk and
capital modeling, as the results of those begin to inform business and management decisions muchmore directly.
The credibilityof each banks approach to measuring credit risk will take on a new importance, both
internally (business lines) and externally (regulators, investors).
This position paper traces the regulatory timetable, explores the implications for management of the k
credit takeaways weve just set out, and notes some next steps for executives who accept the logic of o
argument.
About the author:
David Samuelsis Managing Director
and Global Head of theCommercial Lending Segment
for S&P Capital IQ. He isresponsible for growing the
segment business includingsetting the commercialstrategy for individual
markets; driving sales and
marketing activity; andproviding thought leadershipwithin the market.
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The Regulatory TimelineThe timeline in Figure 1 shows how, in reality, many banks around the world will be assessing the
implications of Basel III at the same time that they struggle to implement, complete or improve the cre
modeling usually associated with Basel II.
Figure 1: TimelineBasel Il second wave and Basel III
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Basel II published(2004)
Basel IIIproposals (2009)
More jurisdictions adopt Basel II(2008-15 and onwards)
Second Wave (2011-2016)
Basel IIIpublished (2010)
Main phase-inperiod(2013-2019)
Capital conservationbuffer phase-in(2016-2019)
Implementationbegins (2007)
Liquiditycoverageratio (2015)
Net StableFunding Ratio(2018)
Source: Prepared by S&P Capital IQ
This suggests that 2012 through 2013 is the time to build a rational Basel II to Basel III strategy rathe
than simply plan ahead for Basel III.
The details of the Basel III proposals and the phase-in timing from 2013 to 2019 are summarized in
Table 1. However, the reality on the ground is likely to be driven by three forces:
The way the rules are implemented and augmented will be key, with potentially large differences inphilosophy emerging between some countries
In turn, a driver of regulatory attitudes will be the economic and political environment over the next
three years including uncertainties about the world economy in terms of EU sovereign risk, strength
the U.S. recovery, and the chances of a China soft landing
Conversely, competitive forces and investor pressure mean that the industry is moving ahead of
regulatory deadlines on capital and liquidity in some areas
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The Basel Committee seems intent on monitoring closely the full, timely and consistent implementatio
of Basel III, which it regards as key to the resilience of the global banking system. The latest in a series
of country-by-country progress updates, published in April 2012, graded the status of Basel II, Basel 2.
and Basel III capital requirements adoption in various categories from draft regulation not published
to final rule in force. So far, countries such as Japan and Saudi Arabia seem to be leading the push to
implement. In the future, we can expect the Basel Committee to monitor continuing implementationby each country, and to look out for differences in implementation at the bank levelincluding
measurement of risk-weighted assets.
It is not yet entirely clear how some key mechanisms within Basel III will be applied in practice, includin
the countercyclical capital buffer. However, most banks would be mistaken to become too wrapped up
in the detail of Basel III. We think that the broader impact of the new regulations is already clear and its
effects on each bank will be significantly determined by a more fundamental force: the efficiency with
which the bank measures and manages its credit risk.
Table 1: Basel IIIKey Elements and Phase-in Arrangements(shading indicates transition periodsall dates are as of 1 January)
2011 2012 2013 2014 2015 2016 2017 2018
As of1 Janua
2019
Leverage Ratio Supervisory monitoring
Parallel run
1 Jan 20131 Jan2017
Disclosure starts 1 Jan 2015
Migration
to Pillar 1
Minimum Common
Equity Capital Ratio3.5% 4.0% 4.5% 4.5% 4.5% 4.5% 4.5%
Capital Conservation
Buffer0.625% 1.25% 1.875% 2.5%
Minimum common
equity plus capital
conservation buffer
3.5% 4.0% 4.5% 5.125% 5.75% 6.375% 7.0%
Phase-in of deductions
from CET1 (including
amounts exceeding the
limit for DTAs, MSRs and
financials)
20% 40% 60% 80% 100% 100%
Minimum Tier 1 Capital 4.5% 5.5% 6.0% 6.0% 6.0% 6.0% 6.0%
Minimum Total Capital 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0%
Minimum Total Capital
plus conservation buffer8.0% 8.0% 8.0% 8.625% 9.25% 9.875% 10.5%
Capital instruments that
no longer qualify as non-
core Tier 1 capital or Tier
2 capital
Phased out over 10 year horizon beginning 2013
Liquidity coverage ratio
Observation
period
begins
Introduce
minimum
standard
Net stable funding ratio
Observation
period
begins
Introduce
minimum
standard
Reproduced from Basel Committee on Banking Supervision, Basel III: A Global Regulatory Framework for More Resilient Banks and BankingSystems, June 2011 revised version, Annex 4, page 69
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Watch out for Basel II Second WaveThere remain many opportunities for banks to improve their performance by completing and selectivel
upgrading their Basel II credit risk modeling projects.
The reason for this is the series of pragmatic decisions some banks have taken in the push to complete
first wave Basel II compliance programs, including:
Focusing on high-priority risks, leaving some important secondary areas until later
Adopting conservative placeholder approaches in tricky-to-model areas to satisfy the regulator
Negotiating regulatory waivers in some areas, the conditions of which may require the bank to impro
risk modelling at some point now fast approaching
Often, banks seeking to comply with Basel II invested heavily in technology, new systems and new ways
to gather data, but invested much less in credit methodology. We believe this balance will reverse durin
the second wave, which will be much less expensive than the first wave but will, in some areas, have a
greater impact on the accuracy of credit risk measurement.
The known gaps and workarounds in credit methodology are one reason why a bank can be Basel II
compliant and yet have considerable second-wave Basel II work to do.For example, a Basel II compliant bank may well continue to use the so-called Foundation approach to
estimating Loss Given Default, or the regulators broad-brush slotting approach to project finance. Yet
more sophisticated approaches are available that can significantly improve the granularity and accuracy
risk measurement in both of these areas. We discuss new approaches to LGD in Sidebar 1.
Sidebar 1
LOSS GIVEN DEFAULTA KEY OPPORTUNITY
For some banks, increasing the accuracy of Loss Given Default estimation represents an important
opportunity to improve credit and capital management.
The key challenge here is that most banks do not have the internal data to generate credible statisticalanalyses of LGDs in each of their businesses. Where the bank has some loss data it is often difficult
to link any record of recovery cash flows to specific collateral types and track and allocate recovery
costs. If enough data exists to make an analysis in a sector, the results may not be granular enough to
differentiate properly between the LGDs of different firms and deal types.
Many banks will also want to make sure their approach complies with strict Basel rules for advanced
approaches to LGD, including the need for one source of data to cover a complete economic cycleno
shorter than seven yearsand for LGD estimates to reflect downturn conditions.
The latest trend in LGD analysis is toward hybrid approaches that can address these various challenge
Hybrid approaches combine statistical analysisbased on internal and external dataand expert judgme
in a rigorous and transparent way to produce results that are accurate and granular as well as being robus
We call one of these hybrid approaches the decision tree approach (Figure 2) because it sets outdistinct paths for the LGD analysis to follow depending on the fundamental r isk drivers in each
structurally distinct portfolio. These include the:
Collateral Path, designed to capture LGD where a loan is associated with a particular collateral type,
and to take into account collateral value and allocation
Seniority Path, for loans to large corporations secured by a general charge on obligor assets, where t
key risk factors are often the degree to which the loan is secured against all assets, the loans senior
and the amount of debt above and below the loan
Specialized Paths for exposures to structurally distinct sectors such as project finance, trade finance, e
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Figure 2: Sample Decision Tree Framework
BorrowerType
Small &
MediumEnterprises
Lending
ValueAllocation
Current Exposure/Facility Type/Maturity
Facility Coverage Ratio
Base LGD Estimate
EAD Estimate
Debt Above/Debt Below
Base
LGD Factor
Sector
Benchmark
Commercial
& Industrial
Other
Sectors
ModelPath
Base LGD Calculation
Collateral
Path
ModelPath
Collateral
Path
Adjustments
QualitativeAdjustments
GuaranteeAdjustment
Downturn
Adjustment
Final LGD
Risk Rating
Overrides
Jurisdiction/Industry
Adjustments
Collateral
Value
Cash
Inventory
Equipment
Real Estate
Marketable
Securities
Accounts
Receivable
Sovereigns
Banks/NBFIs
Project Finance
Public Sector
Enterprise
Unsecured
Subordinated
Secured
All Assets
SeniorityPath
Specialized
Path
Collateralized?
Yes
No
Source: S&P Capital IQ
The decision tree approach allows the bank to use a judicious combination of internal and external
data to explore the effect of each relevant risk factor (e.g., collateral type). The results of the statistica
analysis can then be benchmarked against industry-wide data, and improved using panels of experts
from the relevant divisions of the bank. These experts may also suggest specific adjustments to help
make the analysis more granular (e.g., to take account of the relative LGD of construction real estateversus permanent real estate lending, or the effect of different types of collateral valuation in the case
loans secured by equipment).
Another useful hybrid approach, where LGD data is sparse, is the asset-based approach. The attraction
this approach is that it builds up a probability distribution of the value of all assets that represent a possi
source of repaymentnot only those that represent the primary collateral to the lender. The approach
therefore does not depend on historical loss data, and can be especially useful for low-default portfolio
and for specialized lending. The approach also accommodates adjustments to the LGD estimates based o
the banks own experience and policies, and takes into account additional risk factors such as jurisdicti
Rather than developing their own hybrid approach, banks may also wish to use off-the-shelf LGD
scorecards, based on the analytical processes of Standard & Poors Credit Ratings and the most recen
regulatory guidance and best industry practices. Scorecards can help fill gaps where there is insufficieinternal data to build a statistical model, and our scorecards are designed to offer fully transparent and
intuitive methodologies with detailed regulatory-related documentation. A range of industry- and asse
class specific off-the-shelf scorecards are now available, offering a consistent approach across asset
classes via the same statistical framework where data is available, and more inputs based on expert
judgment whenever recovery data is limited.
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Furthermore, areas designated secondary in the Basel II first wave program may now form a growing
percentage of the banks portfolio. Many banks in the Asia-Pacific region will soon need to upgrade the
approach to analyzing cross-border portfolios, which have grown larger and look riskier than they did a
few years ago (e.g., following the Arab Spring in the Middle East and the recession and banking sector
problems in Western economies).
An even less welcome reason for the second-wave of Basel II projects is that banks have now had time bed down some of their new Basel II models and examine how well they are performingand the result
sometimes disappoint.
On occasion, both business lines and executives have lost confidence in particular models. The models
need to be upgraded and sometimes replaced.
At other banks, there may be disagreement between managers of business lines and risk management
staff over whether a particular model works. The business lines offer anecdotal evidence that the mode
wrong, and the risk group maintains it has looked at the model process and that all the boxes are ticked
The problem often ends up on the desk of senior executives who have no easy way to tell who is right.
Tactics to Manage Basel II Second Wave Efficiently
We think the answer is for executives to take a long, dispassionate look at their Basel II credit framewoand adopt one of three key tactics:
1. Where there are known gaps and internal agreement about particular weaknesses, the bank should
act quickly to prioritize and solve the problems before they affect the credibility of the banks credit
framework and begin to cost the bank real money in terms of scarce capital. As we discuss in the ne
section, Basel III and the growing cost of capital means that inaction is about to become expensive
2. Where there is disagreement over a credit models performance, banks should apply a respected
external model to benchmark the internal models results and to probe for areas of weakness. Ideal
a third-party should work with both the risk and business line teams to mend problems and gain
internal credibility for the banks improved approach.
3. Where there seem to be multiple areas of weakness and the causes are uncertain, executives shou
consider a more wide-ranging review. The real cause of a problem can lie in a number of areas andmay not strictly be a problem with how models have been builtas we discuss on Pages 13-15.
Executives should watch out for one common pitfall in any review. The bank should not simply ask its
internal team to look again at known gaps. The team may not, for example, identify the banks simplistic
approach to project finance as an opportunity for improvement because it knows the bank lacks the
internal resources to mend the problem. To prioritize programs appropriately the bank should first look a
the wider universe of methodologies and data sets that has evolved rapidly since first wave Basel II.
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Basel III Big PicturePressure on ProfitabilityFor many banks, the cost implications of Basel III are significant but will take effect over a relatively
prolonged phase-in period that will allow strategic decisions to be made. Over the 2013-19 phase-in
period, regulatory capital and funding costs will ratchet up as a result of the need for:
More capital, including a new capital conservation buffer and, at the discretion of the national
regulator, a countercyclical capital buffer
Improved capital quality (more common equity and Tier 1 capital, and disqualification of some capita
instruments)
More conservative liquidity regimes, including the Net Stable Funding Ratio and Liquidity Coverage Ra
In particular, when the complete package is implemented as of January 1, 2019, banks will require
minimum common equity of 4.5% of risk-weighted assets, rising to 7% when the mandatory capital
conservation buffer of 2.5% is included. This is a radical shift from the 2% required under Basel II.
The conservation buffer can be drawn down in difficult times. However, banks that do not hold the full
buffer will be penalized through various restrictions, e.g., ability to pay dividends, bonuses, etc.
Regulators will also be able to call on banks to build an additional countercyclical buffer if they think ris
are on the increase and credit growth is too strong. Changes in the treatments and definitions of certa
risk assets will also increase the capital banks are required to hold (e.g., securitization, contingent cred
lines, etc.).
To help alleviate the risk of further liquidity crises, banks will also need to hold enough quality liquid
assets to cover cash outflows for 30 daysthe Liquidity Coverage Ratioand lengthen and stabilize
funding so that they can meet the new Net Stable Funding Ratio. Some banks may also find themselve
constrained by the new non-risk based 3% leverage ratio.
The net result of this is that as banks move through the phase-in period from 2013 to January 2019,
they will need more capital and incur greater funding costsand they will need to improve profitability
to pay a decent return on that capital. The significant impact of the new regulations on the industry as
whole, and the relative effect of each regulatory reform on larger and smaller banks, can be seen in theperiodic impact studies published by the Basel Committee (e.g., April 2012 ).
Far from doing away with internal risk based models, Basel III makes it even more important to get them
right because the increases in capital amount and quality will magnify the cost of any inaccuracies.
Meanwhile, margin pressures including those from an increase in funding expense will make these
unnecessary costs harder to bear.
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It is tempting for banks to wonder if there will be an escape route as the pain from Basel III intensifies. W
think the only escape route is to build a more successful and disciplined business for the following reason
Banks in some developed economies have been helped by a sharp fall in loss provisioning over the la
couple of years, but this was short term relief and it is fading fast
Even if the economy recovers, there are no expectations of a return to rapidly growing GDP or creditbubble economies of a kind that can support poorly performing banks
Regulators are intent on reining in banks that try to leverage their way to profitability and will enforce
discipline through various new mechanisms from leverage ratios to countercyclical capital
National regulators everywhere are improving consumer protections and taking away any easy mone
opportunities
Meanwhile there is the competitive threat of a significant, unregulated shadow banking system as w
as regulator determination in some countries to foster competition within the regulated sector
If there are no easy answers to the pressure on margins, banks will instead have to focus on running th
businesses more efficiently after taking account of risk and capital costs.
Business ImpactIn a world of scarce and expensive capital, banks will need to raise their game and concentrate resourc
on what they do really well through:
Focusing on the areas with the best return on capital, which means understanding their key risk and
regulatory capital costs betterwith the emphasis for most banks on credit risk
Gaining operating efficiencies (cost, speed, flexibility) in terms of credit process, credit modeling and
credit risk management
Proving to regulators and investors that they really are doing the right thing
This means doing more than credit optimizationa phrase quickly becoming as popular in regulatory
and government circles as tax avoidanceas useful as this may be for some banks.
It means making sure that the bank measures profitability after taking proper account of the costs of
risk which, in turn, means measuring risk accurately and making sure this drives bank decision making
from the tactical to the strategic level. Meanwhile, banks should plan how to involve stakeholders in the
business strategy and capital planning through improved risk transparency and model validation program
In practical terms, this will mean working through the following five-step plan (Figure 3):
1. Defining risk appetites, related bank policies and strategic planning at the top of the bank
2. Forging a link from these high-level goals to actionable levers such as capital allocation, limit settin
and risk-adjusted compensation
3. Improving risk measurement and risk-adjusted profitability analytics, especially through better and
more granular credit modeling, to make sure the levers work and are closely linked to the reality of t
banks credit exposures
4. Validation of key risk models to maintain all-important credibility with third parties and also with
business lines
5. Ongoing risk monitoring, especially through more efficient credit processes and platforms and
forward-looking surveillance systems, so the bank can react faster and do more for less
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Figure 3: Five Steps Toward Improved Sustainable Profitability
Risk Appetite
and Policy
Management
Levers, e.g.,
Capital Allocation
Risk and
Profitability
Measurement
ValidationMonitoring and
Surveillance
The rest of this position paper looks at these in more detail.
Credit Risk Appetites and Management LeversIn response to the financial crisis, banks are being pushed to set out formal risk appetites including how
much credit risk they are willing to assume in the course of doing business. For many banks, defining ri
appetite centers on strengthening board oversight and giving investors a better sense of the risks the
bank intends to run.
These goals are laudable but we believe that the real challenge lies in how to link risk appetites to
decisions taken further down the business chainthe decisions that actually determine the risks the
bank runs.
This means framing the risk appetite in a way that can be linked to the credit metrics and more genera
risk language used in the day to day management of the bank. It also means asking difficult questions
about:
How the bank organizes itself to develop, implement and monitor its risk appetite, from setting up
specific risk appetite task forces to assigning specific duties and accountabilities to C-level executiv
risk committees, and business level functions
How the banks stated risk appetite influences not only its limit framework, but also its business
strategy, capital management and allocation, capital planning, incentive compensation structures, rmanagement policy, and other key mechanisms
We believe that executives should look at the banks risk appetite program as a way to make sure the ba
has a logical analytical and organizational framework for taking different kinds of risk-related decision
consistently at different levels of the bank. Many different forces in the banking industry are pushing
banks toward the same end. For example, Sidebar 2 explains how remuneration will be linked more close
to long-term performance in the future, taking account of risk costs.
Forging a closer link between risk and key management levers such as remuneration will cause some
antagonism within banks. One of the key tasks of bank executives over the next few years will be to find
the right balance between limiting risk and pursuing reward in the current business environment. Havin
found the right balance in principle, they will need to convince business lines that the banks key risk
models are accurate and trustworthy.
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Sidebar 2
TRENDS TOWARDS RISK-ADJUSTED REMUNERATION
Since the financial crisis, there has been a persistent push from regulators and policy makers to ensur
bank remuneration is structured in a way that reduces the incentive for bankers to take excessive risks
For example, in April 2009, the Financial Stability Board published nine principles for sound
compensation practices; in December 2010, the UKs Financial Services Authority published the final
rules of its Remuneration Code; and in May 2011 the Basel Committee surveyed the methodologies
available to align remuneration with bank risk and performance.
Policy makers believe that better-aligned remuneration is a good thing and that it is one of the key leve
through which a bank can implement any board-level statements on risk appetite and long-term bank
soundness.
The mechanisms for supporting best-practice remuneration fall into three main groups:
Outlawing of various bad practices (e.g., rewards for failure)
Increased use of shares-linked schemes and deferral periods
Encouragement of qualitative and quantitative risk adjustment of compensation
Quantitative risk adjustment of compensation, in particular, has been drawing increasing attention from
policy makers and leading banks. Quantitative techniques for adjusting remuneration are potentially
quite varied but attention has focused on using capital costs and economic capital calculationsof
various kindsto adjust compensation. The illustrative example to the left was included in the Basel
compensation methodology paper.
We believe that the new focus on risk-adjusted compensation will drive banks to revisit and improve th
fundamental approaches to credit risk for three key reasons:
First, for many banks, success in managing credit risk is the key driver of bank performance over the
longer term, and will directly impact compensation, e.g., the likelihood of executives receiving their fu
allocation of deferred payments
Second, credit risk is a key driver of the banks economic capital model and, therefore, of many of the
quantitative mechanisms now coming to the fore
Third, senior executives using risk-adjusted compensation to align their banks business decisions
with board-approved risk appetite will need to gain and retain the confidence of key personnel in how
compensation is calculated
Illustrativeexample
A large, internationally active
bank offers a full range of
financial services from retail
banking business through to
a complete suite of wholesale
banking services and funds
management. Variable
remuneration is determined
on the basis of an economic
capital model. Further, the
bank is expected to adopt
both ex-ante and explicit
ex-post risk adjustments that
are appropriately aligned to
risks. There must be a clear
methodology for allocating
variable remuneration from
group level to business units
and individuals. Variable
remuneration should include
a substantial deferred
component
Excerpted from Range of
Methodologies for Risk and
Performance Alignment
of Remuneration, Basel
Committee, May 2011, p.15.
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Improving Credit Risk ModelingDiscussions about improving credit risk measurement often turn directly to the important topic of
increasing the accuracy of credit models, but there is a bigger topic to address first: Is the bank
organizing itself to succeed?
This isnt just a matter of getting the right organizational chart, but whether the bank is deploying the
right amount and kind of resources to win the credit modeling battle in three key areas.
1. Shaping credit approaches to todays strategic reality
Management can take a hard look at how the bank is deploying credit resources and policies, makin
sure this reflects where the bank makes its money and takes its biggest risks, rather than remainin
trapped in yesterdays strategic reality. Many banks have changed strategic shape since the financi
crisis, and others will follow over the next few years.
In particular, banks should be applying their best internal talent to high-value, high-return credit
tasks linked to the banks competitive and specialist strengths, rather than in areas where it would
more efficient to leverage third-party solutions (e.g., benchmarking, data sourcing, model building)
2. Assessing the latest range of credit approaches open to the bank
Not all banks have begun to take advantage of the extraordinary growth in credit data and creditresearch technologies over the last few years.
For example, has the bank reviewed the new credit methodologies that have become available in areas
such as project finance risk and other low default portfolios and has it looked at the greater availabilit
of external data when developing credit models? Some banks are continuing along traditional and cos
paths simply because their internal teams are unaware of global developments in credit methodology.
3. Creating efficient credit information and workflow
Modern banks need to organize their overall credit rating and scoring process so that it is efficient,
logical and transparentand up-to-date information is instantly available to create the key
management reports right across the bank.
Once the bank has begun to think about these big picture issues, executives should also take a
forensic look at how the bank has built up its family of credit approaches and models across differe
business lines.
Often the model family has grown organically over a number of years and, in our experience, banks can
benefit from tackling a number of common shortcomings:
Too little granularity in risk modeling
Too much emphasis on financial ratios
Inconsistent use of qualitative and non-financial elements
Ineffective models for low default sectors
Structural issues such as the likely effect of parental support
Incomplete or inaccurate mapping between ratings and PDs
We discuss these shortcomings in more detail in Sidebar 3.
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Sidebar 3
TOP ISSUES WHEN ASSESSING APPROACHES TO CREDIT RISK MODELING
Too little granularity in risk modeling
The degree of granularity to apply is often a challenge for banks in their risk modeling, particularly
determining the right number of sector-specific credit models. Sometimes, banks try to apply one
model too widely, which can result in inaccurate ratings for certain industries or the manipulation of th
model by the analyst. Other banks build many models but the growth of their model family is often driv
by the desire of a central modeling group to apply their quantitative skills rather than the credit insight
that we believe should inform model methodologies.
Our advice is that the right number of rating models is a function of the risk profiles and portfolio
significance of the banks obligors. If the underlying risk factors are the same and the risk profiles are
homogenous, then the same rating or modeling approach can probably be adopted. If they are not, the
a different approach needs to be crafted.
Too much emphasis on financial ratios
Banks are often tempted to use financial ratios as a proxy for creditworthiness, not least because
the analysis of financial ratios tends to be quick, easy and cheap. As every bank analyst understands,
however, other factors are at least as important including the industry sector a company inhabits, the
companys competitiveness within that sector, and quality of management.
These other risk factorswe call them business riskshave a complicated relationship to financial
risk. For instance, a company with a very stable industry risk profile might be able to support a relativel
weak financial profile. Many of the problems we see in bank modelling lie with misunderstandings of
how best to assess non-financial risk and then combine this assessment appropriately with the banks
assessment of financial risk.
Inconsistent use of qualitative and non-financial risk elements
The business risk dimension plays a critical role in determining the credit risk of an obligor. It is often
the major differentiator between obligors of relatively high credit quality, and helps to make a rating
more forward-looking. However, non-financial risks are difficult to assess in an objective and consistenmanner. Lenders may not all consider the same risk factors, take into account the same criteria for eac
risk factor, or weight them in the same way in their final decision.
The answer may be to apply sector-specific scorecards that guide how the expert arrives at a decision
That way, the correct risk factors can be considered and scored using objective guidelines for each
criterion, leading to a much more consistent, transparent and replicable rating process.
Ineffective models for low default sectors
Portfolios that exhibit low rates of default (e.g., lending to large corporations, financial institutions,
public sector enterprises, commercial real estate) rarely generate enough internal default data to
support robust statistical analysis. However, when banks try to apply external data, there is a danger th
the chosen data will not capture the true risk characteristics of the portfolio in question.
There are a number of answers to this problem, including a more careful sourcing of better external data
and the recognition that banks must sometimes rely on expert judgment-based models that do not ov
rely on statistical analyses but instead leverage the judgment of experts in a rigorous and objective way.
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Coping with structural issues such as parental support
Some credit assessments are complicated by structural issues including an exposure to holding
companies, new subsidiaries, affiliates, joint ventures, relationships to government, and the like. The
effects can be positive (e.g., parental support) or negative (e.g., concerns over how cash flows might be
allocated between subsidiaries in a given situation).
Statistical models, especially those largely reliant on financial information as predictive factors,seldom cope well with structural complexities. We counsel that structural issues must be treated in a
rigorous and methodical way that draws out the exact impact on the credit (e.g., through using overlay
approaches that apply objective, consistent criteria to help analysts to adjust a standalone rating to
account for structural issues).
Incomplete or inaccurate mapping between ratings and PDs
Given the small amounts of internal data that are usually available, one of the key challenges facing
banks is how to map an internal rating to an objective rate of default. This mapping is useful for severa
reasons including the calculation of bank capital adequacy using Basel IIs Internal Rating Based
approaches.
One key approach here is to map the banks internal ratings to ratings generated by the Standard &
Poors rating agency, which in turn are associated with a long and rich history of defaults. However, this
process must take account of any methodological differences between the banks approach to rating
and the approach adopted by Standard & Poors.
Credibility is KeyValidating the ModelsExecutives should look on validation as a wide ranging tool not only for increasing the accuracy of
the banks approach to risk measurement but for building credibility and confidence in the banks risk
measurement approach.
The validation process includes an independent review of the structure, calibration, performance and
operation of a risk system (e.g., internal rating system) as well as scrutiny of the model construction
process and methodological soundness.
This is vital if the output is to be used to make major business decisions (e.g., on portfolio management
and to drive key management levers such as assessments of risk-adjusted profitability and
remuneration.
The process of validation of internal rating systems is too often looked on as a set of highly technical
tests. However, regulators prefer to use the term mosaic of evidence to describe the validation
process, in order to capture the many dimensions of robustness that it must address (Figure 4).
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Figure 4: Review of Validation Framework
Model development/revisions
Validation of conceptual and theoretical soundness
Model enhancement opportunities(based on validation activities)
Confirmation ofmodel operation
Outcome analysis(backtesting & benchmarking)
VALIDATION PROTOCOLS
ENHANCEMENTS
Source: S&P Capital IQ adapted from Validating Internal Rating Systems, RMA Journal December 2008.
Governance framework
Perhaps the most important dimension is the validation of the conceptual soundness of a modelis
it conceptually the right tool for the job? This means analyzing whether the model captures the key
risk characteristics of the portfolio in question in terms of the assumptions that underlie all statistical
models and also reviewing any model construction issues. For example, was the model developed usingset of data that reflects the risk characteristics of the portfolio in terms of:
The period of time the development data was gathered
The kind of entities in the data
The geographical focus of the data
It is vital that the validation team has a background in fundamental credit analysis across many differe
kinds of portfolios, as well as modeling expertise. Its this that allows the team to understand where
modeling and data assumptions might be incorrect. It can also be extremely helpful to benchmark a
banks internal model by conducting an independent assessment of credit or by comparing the results
those of a respected statistical model.
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The way the bank uses its model day-to-day is another dimension that must be covered, and this is wh
it is so important that validation teams understand how the model is applied by the business line. For
example, a test might show that the models estimation of default rates is particularly poor for credits
in the lowest grade of Pass. However, does this reflect a technical problem with the model, or does it
reflect pressure on the rating process as the business line struggled to attain targets? If the latter, the
real solution to the problem might lie in the bank coming to a more realistic trade-off in terms risk andrewardinsisting on a more rigorous rating process while allowing the business line to accept slightly
lower quality credits in return for mitigating factors or increased premium.
Finally, validation should include a wide range of outcomes analysis, including various forms of statistic
back testing (e.g., discriminatory power, calibration tests) and benchmarking (e.g., against third-party
models). The aim here is to use various tests to explore the strengths and weaknesses of the model.
Ideally, validation should support a program of continuous improvement for the banks credit models
rather than taking the form of an annual pass or fail.
Over the next half decade, banks will find that they need to apply validation skills to improve and create
confidence in a widening range of models that have become business critical, including economic
capital, RAROC-type models, and risk-adjusted remuneration calculations. Increasingly, banks will see
that building confidence internally in how risk is measured in key lines of business is as important anobjective as regulatory compliance.
Ongoing Risk Monitoring and SurveillanceWe believe that improving credit risk management at banks is not simply a matter of improving credit
models. Banks need to look beyond this to build efficient credit platforms, rapid credit surveillance
systems, and a framework that facilitates forward-looking management decisions.
Efficient credit platforms
Building more efficient credit platforms is important for a number of reasons. One is that, at the mome
senior managersthe end users of ratings in their efforts to control bank-wide riskoften feel they lac
control over the consistency and quality of the ratings process. They also find it difficult to extract the
credit information they need to quickly manufacture key management reports.In the past, banks have sometimes thought the solution lies in a centralized piece of ratings software,
but this approach rarely offers the kind of analytical flexibility that is necessary for accurate ratings
across many different portfolios.
Instead, the solution must lie in a more flexible, integrated approach to credit process infrastructure
that offers the benefits of centralizing credit information without putting unnatural constraints around
analytical methodology.
The optimal credit platform would bring together the right risk data, research, analytics and efficient
workflow capabilities to facilitate a scalable, consistent approach to internal ratings based on accurate
assessments of the three key analytical components of credit risk and internal credit ratings: probabili
of default (PD), exposure at default (EAD) and loss given default (LGD).
Validation should supporta program of continuousimprovement for thebanks credit models
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We recommend a single central framework that can ensure global consistency where this creates value
while also accommodating the best-of-breed local components (e.g., local analytical and modeling
approaches, local data and inputs) capable of capturing the unique aspects of credit assessment in ea
industry sector and geographical region.
Meanwhile, the central framework should make instantly available to management the credit outputs
necessary for understanding the bank-wide credit portfolio, so that enterprise credit risk managementbecomes a more practical reality.
Rapid credit surveillance
Credit ratings executed at the time a major loan is made need to be thorough and accurate, and they a
inevitably relatively time consuming and expensive to makehowever efficient the process.
However, obligor and counterparty risks are driven by an increasingly volatile and interconnected world
evidenced most recently by the global fall-out from the banking crisis and the rapid emergence of
sovereign and country risks in Europe and the Middle East.
We think this means that firms must develop a lighter, rapid form of always on credit surveillance,
distinct from but complementary to the firms internal rating system.
Its purpose would be to alert management to the impact of an event or trend and then direct the heavycavalryexpert-judgment based credit analysis and senior decision makerstowards vulnerable areas
of the portfolio. Such a system would have to be fast and always on, economic so that it could be
applied across the whole portfolio, and comprehensive in that it should cover most of the large corpora
portfolio including private firms and firms that lack a public rating.
The risk measures used in the system will need to be computed rapidly. This argues for the inclusion of
Company-specific risk measures that can be automated (e.g., automated feeds of select financial
information, where available, to measure the key risk dimensions of a company such as profitability,
capital structure, and liquidity)
Relevant market information (e.g., property or commodity market indices)
Systemic risk measures (e.g., industry risk and country risk) with scores that are judgmental in natur
but that are applicable to multiple counterparties (and which may be supplied most efficiently by thi
parties)
Many of the information components of such a system are now readily available. We think it will not be
long before the first such systems are put into practice, allowing banks to identify key challenges in the
portfolios much more easily, quickly and systematically than at present.
Framework for forward-looking risk-based decision making
The combination of improved application of r isk appetite to management levers, efficient and
transparent credit platforms and better monitoring and surveillance will begin to provide leading banks
with the infrastructure they need to make truly forward-looking risk-based decision making.
Until recently, many organizations largely invested in risk management improvements to please their
regulators rather than to improve their business decisions. The reasons for this were complex, but lay
partly in the relatively short-term horizons adopted by some banks and their investors, misjudgments
about the severity of some risks, and the difficulty of measuring risk accurately.
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We believe the industry is reaching a tipping point where a new determination to avoid cyclical booms
and busts, a greater understanding of the destructive power of risk, and new, efficient ways to measure
risk will encourage banks to use their investments in risk management to drive business strategy and
business selection.
The acid test will be whether risk measurement begins to be regarded not as a restraint on business,
but as a competitive advantage in helping the bank focus on business with the greatest risk-adjustedrewards. Table 2 sets out some of the specific ways in which improved credit analysis can be turned int
better tactical and strategic decision making by the bank.
Table 2: Improved Credit Risk Assessment SupportsRisk-Based Decision Making
RISK-BASED DECISION MAKING ROLE OF IMPROVED CREDIT FRAMEWORK
Optimized credit approvals, credit
selection and credit limit setting
For example, improved credit scores, PDs and improved LGDs
can be applied actively and dynamically in setting and monitoring
credit limits. Improvements support economic capital-based lim
setting and concentration risk management
Internal capital adequacy
assessment
Improved credit risk measurement is the key input for internal
capital adequacy and accurate economic capital analysis
Risk-based pricing and deal
structuring
Accurate, granular credit measurement supports differentiated
pricing (e.g., economic capital-based) and optimal deal
structuring (e.g., optimal trade-off between customer PD and loa
terms such as amount and quality of collateral)
Business line capital allocation,
RAROC analysis, and business
strategy
Business unit evaluation and planning based on accurate
assessment of credit costs; improved product and customer
relationship profitability analysis incorporating true risk costs
Risk-based remunerationAccurate, internally credible credit risk analysis is a key pillar for
risk-adjusted remuneration strategies in credit businesses
Efficient credit platformSupports quicker, better portfolio and deal decisions and
reduction in underwriting costs
Source: S&P Capital IQ
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Conclusion: Turning Second-Wave Basel II and Basel III to Your AdvantagBank executives around the world can be sure that second-wave Basel II and Basel III will reshape their
businesses in three key ways:
1. Capital is going to become more scarce and expensive and that means the bank must measure risk
more accurately to work out which businesses to stay in and which to leave. Improved credit models
and data are critical components of this effort.
2. These risk measurements are going to increasingly drive management levers including capital
allocation and risk-based pricing, and extend into areas such as remuneration. Model outputs must
gain the confidence of internal as well as external audiences.
3. Banks need to make the connection between second-wave Basel II projects, Basel III capital
pressures, a tougher banking environment and the need to encourage businesses to help shape
investments in credit management.
The growing stream of second-wave Basel II projects are not simply finishing-off exercises, but the
beginning of a new round of improvements that will focus on improving credit methodology and
accuracy, rather than systems and technology. Table 3 sets out some of the next steps open to
management.
Table 3: Next Steps to Tackle Basel II Second Wave and Basel III
ISSUE ACTION
Known gaps and weaknesses in existing
model family
Draw up a list of possible areas of concern and
improvement after a full validation including gap
analysis and after considering the full range of new
methodologies and the availability of external resource
not previously identified
Contention over where weaknesses existConsider using benchmarking and independent opinion
to judge the scale and true identity of the problem(s)
Lack of a strong connection between risk
appetite and bank credit and business
strategy
Look at how to link credit output to the banks riskappetite and management levers discussed in this
paper, including making credit information and
processes more transparent to senior executives
Doubts expressed about the banks credit
estimation approach in particular sectors
by regulators or by business leaders
Build confidence by rigorously validating models in
line with Basel II/III standards; supplement internal
validation with external opinions
Growing capital costs due to Basel III
putting business model under strategic
pressure
Improve the credit framework to better reflect capital
costs (e.g., improved LGD estimation), ensuring the
bank knows which areas make risk-adjusted money and
which should be exited; implement risk-based pricing
and compensation
Need for greater operating efficiency
Consider whether valuable internal credit expertise is
used effectively or should be leveraged by improving
credit workflow or using new credit scoring technologie
in key areas
New strategies and growing portfolios in a
changed economic and risk environment
Conduct gap analysis to make sure sophistication of cred
methodologies has kept pace with the banks changing
shape and growth areas (e.g., cross-border portfolios)
Source: S&P Capital IQ
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Approached correctly, taking these steps will allow banks to prepare and reshape their businesses in
advance of Basel III, heralding a longer-term shift towards business-driven rather than purely regulato
driven investment in improved credit management.
With hindsight, the next few years will be looked on as a moment of transformation in bank credit
management that will decide the winners and losers in a tougher, transformed business environment
one that offers many opportunities for banks that position themselves to be industry leaders.
How S&P Capital IQ Can Help You
Our Credit Risk Analysis Solutions
Serving the credit risk needs of thousands of clients globallyfrom emerging to developed markets
S&P Capital IQ provides solutions that support the critical tasks associated with your daily workflow as
well as your strategic imperatives. From key risk data and credit risk models for scoring to a detailed
analysis of default and recovery, we can help you improve your strategic approach to credit risk and gai
important insights on rated and unrated entities at the individual and portfolio level.
Our organization offers a comprehensive set of integrated services delivered by teams of regionally
based experts. From risk frameworks and model validation to benchmarking and outsourced credit
analysis, we have the right local skills and capabilities to complement your internal teams. Let usassist with your efforts to achieve regulatory approval, drive capital and liquidity management and to
implement and drive risk-based decision making at all levels.
Using our analytic tools, data and services, now you can easily and cost-effectively:
Quantify the credit risk of borrowers and collateral
Benchmark internal credit ratings against a globally recognized metric
Develop appropriate documentation, including credit memos
Calculate the credit risk contribution for the pricing of loans
Adjust the levels of collateral based upon the borrower and facility risk profile
Expedite surveillance and identify weakest or marginal credits for review
Drive effective limits, capital and liquidity management
Leverage our global service teams to bring independent expertise and scale to your risk projects
Identify opportunities
Using our dynamic screening tools you can search for target borrowers on a wide range of data points,
including risk measures. Once you have identified those you will be able to, conduct peer analysis on a
relative and absolute basis looking for business opportunitiesincluding industries, geographies and
specific borrowersthat meet your key criteria. Leverage our comprehensive industry and borrower
research to help complete your overall view.
Parameterize credit risk models
Your models, and ours, use financial and economic information to create estimates of a borrowers
creditworthiness, probability of default (PD) and loss given default (LG D). Capture inputs to drive the
models from our rich data sets, including:
Raw fundamental data and spread ratios for rated and unrated entities
Fundamental data adjusted for non-recurring charges to enhance comparability
Industry and borrower peer group ratios
Macroeconomic factors
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Analyze with models
The integration of default and recovery tools with scoring models produces dynamic risk measures.
Credit scores are generated using our comprehensive library of industry-specific models covering
over 40 countries, including developed and emerging markets; create scores for all borrowers with
revenues greater than U.S.$25 million where you have financial statements or access more than
50,000 pre-calculated scores
Forward-looking, one-year PD estimates cover privately-held small and medium enterprises (SMEs)
in the U.S., Canada, U.K., France, Spain, Germany, Italy and Greece and the public market in the U.S.;
create PDs for all borrowers where you have financial statements or access more than 1.8 million
pre-calculated PDs
Use credit assessment scorecards to assess borrower and facility risks for specific industry segmen
when there is insufficient data to build a quantitative model. Based on Standard & Poors credit ratin
methodology, your internal methodology or both, they include qualitative and quantitative factors an
associated weightings that produce a numerical score that can be mapped to the Standard & Poors
rating scale, an internal rating scale or a PD
Internal facility-specific LGD assessments in the low default environment. They cover a wide range of
asset classes in the traditional and specialized lending areas (and the main relevant sub-sectors)
corporates, financial institutions, public finance, project finance, asset finance or real estate. Tools
are applicable and customized globally and give LGD estimates for all relevant scenarios, including
debt restructuring, liquidation and 30-day post-default selling of the facility. Scorecards constitute a
effective and robust solution to the cases where there is insufficient data to build a statistical model a
have proven to be widely recognized Advanced Internal Ratings Based (AIRB) models by several regulat
Our web-based model forecasts LGD values by applying an econometric framework to the 4,000+
recovery estimates in our database of ultimate recovery data and distressed debt trading-price
information; it enables estimation of recovery after restructuring of debt, liquidation and 30-day pos
default recovery (by selling the asset) for specified defaulted facilities; while the model is trained on
U.S. data, it has been proven a useful benchmark tool for global application
Determine the anticipated path to improvement or default by evaluating the impact on normal cour
of business of down-turn and up-turn scenarios; Input Sensitivity Ranks show which variable or rat
has the largest impact on changes in the current credit score for the specific scenario
Perform additional severe scenario stress testing for less frequently occurring events. Automatica
adjust financials based on a pre-determined light or heavy stress scenario and scores will be
mapped to stressed PDs for one, three and five years
Use pre-calculated scores and PDs and efficient quantitative models to undertake timely and efficie
surveillance of your credits, providing the right early warning indicators and actionable information t
drive pro-active credit monitoring
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About S&P Capital IQ
S&P Capital IQ, a business line of The McGraw-Hill Companies, Inc. (NYSE:MHP), is a leadingprovider of multi-asset class and real time data, research and analytics to institutional
investors, investment and commercial banks, investment advisors and wealth managers,
corporations and universities around the world. We provide a broad suite of capabilities
designed to help track performance, generate alpha, identify new trading and investment
ideas, and perform risk analysis and mitigation strategies. Through leading desktop
solutions such as the S&P Capital IQ Global Credit Portal and MarketScope Advisor
desktops; enterprise solutions such as S&P Capital IQ Valuations, and Compustat; and
research offerings, including Leveraged Commentary & Data, Global Market Intelligence,
and company and funds research, S&P Capital IQ sharpens financial intelligence into the
wisdom todays investors need. For more information visit www.spcapitaliq.com.
Benchmark results
Extensive data enables you to:
Analyze empirical statistics compiled in our ongoing credit studies to assess ratings migration, and
default and recovery rates across geographies, regions, industries and sectors. History back to 1981
covers more than 15,000 issuers, 130,000 securities, 150,000 structured finance tranches and 140
sovereigns
Compare results for the rated portion of your portfolio to our detailed credit ratings and research tha
includes more than 9,000 global entities with history back to 1922
Assess peer group averages with our broad and deep financial statement information and
pre-calculated ratios
Decide your course of action
Finalize your opinion and automatically create a wide range of reports covering all the topics you need f
a comprehensive credit memo, including:
A description of the business and a brief discussion of its future prospects
Major rating factors and the rationale behind the model outcomes
Graphs of ratings trends, up to three years of CDS history and the sector outlook
Detailed tables for ratings and financial peer group comparisons
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Copyright @ 2012 by Standard & Poors Financial Services LLC, a subsidiary of The McGraw-Hill Companies, Inc. All rights reserved. Thismaterial was prepared by S&P Capital IQ.
Standard & Poors Financial Services LLC, its affiliates (collectively, S&P) and their third-party providers do not guarantee the accuracy,completeness or timeliness of any information, including model, software or application, they provide, and are not responsible for errors anomissions (negligent or otherwise), or for the results obtained from the use of such information. S&P and its third party providers disclaim
and all express or implied warranties.The analyses, including any ratings, of S&P contained herein (Content) are statements of opinion as of the date they are expressed andnot statements of fact. S&P assumes no obligation to update the Content following publication in any form or format. The Content is nota recommendation to purchase, hold or sell any security, or to make any investment decision or pursue any financial strategy. The Contentshould not be relied on and is not a substitute for the skill, judgment and experience of the user, its management, employees, advisors and/or clients when making investment and other decisions. S&P does not act as a fiduciary or an investment adviser, except where registered asuch.
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