Basel II to Basel III

8/12/2019 Basel II to Basel III

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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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S&P CAPITAL IQ FROM SECOND WAVE BASEL II TO BASEL III

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



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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)



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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.

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Basel II to Basel III

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