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FINANCIAL SERVICES Managing Credit Risk Beyond Basel II ADVISORY

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FINANCIAL SERVICES

Managing Credit RiskBeyond Basel II

ADVISORY

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Contents

Foreword

Delivering on a PromiseHow to Leverage Basel II Investments to CreateValue for the Credit Business

Laying the FoundationsSound Data Governance and Data Quality as Assetand Competitive Advantage

In Search of the Right MeasureLinking Credit Risk Modeling and Measurement inBasel II and IFRS

Going to the LimitOn the Use of Advanced Credit Risk Measures in aCredit Risk Limit System

Exploring the UnexpectedDeveloping and Using Credit Risk Stress Testing inRisk Management

Pricing at all CostsComments on Credit Pricing Today and its Post-Basel II Future

Growth ProcessesLeveraging Basel II Scoring Models and Data toImprove Credit Processes

An Orchestrated Approach to Value CreationOpportunities and Challenges in Active CreditPortfolio Management

Calculations with Many UnknownsThe Implications of Credit Derivatives for CorporateRestructurings

The Value in Bad DebtOn the Management of Sub- and Non-PerformingLoans in Retail Banking

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Many banks have invested significantlyin improving their credit risk manage-ment in the past few years. Specifically,banks have invested in methods,resources, processes, and technologyto assess, monitor, manage, andmodel their credit risk. Most of theeffort has focused on compliance withBasel II and other regulatory require-ments, and some banks continue tostruggle as they work through theapproval process.

Leading banks, however, already haverisk management frameworks in placeand are now seeking to make theprocess significantly more relevant tomanagement decision making.

These banks are evaluating how tobuild on lessons learned from Basel IIimplementation, regulatory approvalpreparations, and regulators’ feedback.An emerging goal is to leverage theirinvestments in credit risk managementto make better decisions and enhancebusiness performance.

In this white paper, KPMG credit riskpersonnel from around the worldexplore these issues. They reviewresults of the Basel II implementationas well as the changes that haveoccurred in the markets for credit risktransfer and present their perspectiveson value creation in the credit businesspost–Basel II.

Specifically, this document addresseskey aspects of credit risk management,such as:

• Coping with the rising amount ofcredit-related data post–Basel IIthrough improved data qualitymanagement and data governance

• Leveraging the Basel II methodology to address International FinancialReporting Standards (IFRS) requirements

• Using internal ratings in credit risk limit management • Devising stress tests as components of sound credit risk management• Addressing pitfalls and challenges in risk-adjusted credit pricing using Basel II

ratings• Using Basel II data to create more value from credit processes• Leveraging opportunities for implementing active credit portfolio management

using leading risk measurement practices and risk transfer techniques• Managing impaired loans in a world of credit derivatives• Using customer information and classification systems to improve bad debt

management.

We hope you find this document useful, and we look forward to discussingthese issues with you.

Jörg Hashagen

Global Head Advisory Financial Services

Managing Credit Risk: Beyond Basel II 5

Foreword

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Institutions that are currentlygaining approval to use theinternal ratings–based (IRB)approach under Basel II havespent millions on their pro-jects and now want to see areturn on this investment.How and where can theinvestment in models, infra-structure, and processes pay dividends as economicconditions evolve? Whereare the opportunities toimprove processes, tostreamline organization, or

Delivering on a PromiseHow to Leverage Basel II Investments to Create Value for the Credit Businesses

1 Managing Credit Risk: Beyond Basel II

to price more appropriatelyin the market? These ques-tions are asked against abackground of credit riskmanagement that, with therise of new products andmarkets, has changedconsiderably since the incep-tion of the Basel II process.

Pia Evertsson (Sweden), Steven Hall(United Kingdom), and JürgenRingschmidt (Germany) explore someareas for leveraging investments inBasel II implementation to createvalue for the credit businesses.

Following years of planning and devel-oping systems, tools, and processes tomeet Basel II requirements, banks willbe able to apply Basel IRB approachesby 2007 or 2008, subject to supervi-sory approval. In fact, most medium tolarge-sized institutions will seek orhave already obtained IRB approval.

Whatever practitioners, risk profession-als, and industry observers may thinkof Basel II, none can deny that it hasled organizations to an unprecedentedinvestment in risk infrastructures. Theresult has been a sizeable change inthe way many banks approach riskmanagement (see Figure 1 on page 2).

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Some of the key benefits are:

• Greater implementation consistencybetween adopting banks

• A common risk language—for exam-ple, “probability of default,” “lossgiven default,” and “expected lossmeasures”—leading to more accu-rate comparisons of various outputsand results

• Greater scrutiny, challenge, andreview of the technical approachesbeing adopted, which has stimulatedan evolution of thinking in the quanti-tative areas of risk management

• A focus on the importance of soundrisk governance, systems, and prac-tices, and an increased understand-ing of how such an infrastructureaffects market perceptions, given the new reporting requirements.

Some institutions are leveraging theirBasel II efforts to drive business bene-fits and competitive advantage. Othershave limited such activity to piecemealinitiatives. Indeed, only a few moreadvanced and sophisticated bankshave progressed to the next level.

This observation is somewhat contraryto the business cases presented whenthe Basel II initiatives were introduced.At the time, besides prospects ofreduced regulatory capital require-ments, improvements in the manage-ment of credit risk and resulting gainsin value for a bank’s credit businesswere cited as equally beneficial reasonsfor making the long and costly journeytoward implementing Basel II’s IRBapproaches. While compliance withregulatory requirements has dominatedefforts so far, now banks want todeliver on the value creation aspect ofthe Basel II business case. This is trueeven for those banks that need to workfor a few more years to reach full IRBcompliance.

While banks were focused on Basel IIimplementation, the environment ofthe credit business has changed. Thesignificant increase in transactionvolume taking place in credit deriva-tives and securitization markets areprobably the most obvious examples ofchange affecting the way credit busi-ness will be conducted in the future.

Managing Credit Risk: Beyond Basel II 2

Figure 1: Going to the Next Level by Leveraging Basel Improvements

Source: KPMG in the U.K., 2007

Figure 2: Global Credit Derivatives Market

Source: Fitch Ratings 2006

Figure 3: Global Securitization Volume

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Purpose and Scope of ThisDocumentAgainst this background, this publica-tion tries to shed light on a number ofareas expected to play an importantrole in the future of credit businessand credit risk management. Onepublication cannot cover so complex afield, but this one attempts to addresssome of the key topics in the creditrisk management of the future:

• Managing data quality. The imple-mentation of Basel II has producedan unprecedented amount of dataon banks’ credit portfolios. Ensuringthe quality of this data and managingdata ownership appropriately is afundamental prerequisite for soundcredit risk management. Thus, thefirst section addresses the impor-tance of data quality managementand data governance.

• Leveraging Basel II knowledge for

IFRS. In addition to complying withBasel II, banks have also had to copewith implementing IFRS. However,among the topics left largely unsolvedin the first wave of the IFRS imple-mentation is the issue of portfolioimpairment. As a first example ofhow the Basel II work can be lever-aged to serve purposes outside thenarrow confines of prudential regula-tion, this document considers howthe Basel methodology can be usedto cope with the IFRS requirements.

3 Managing Credit Risk: Beyond Basel II

• Limiting credit risk. Then, turning tocredit risk management proper, thisdocument explores a number ofissues related to risk managementand modeling, beginning with thepossibilities for introducing morerisk-sensitive credit risk limitsystems using the methodology andmodels developed under Basel II.

• Implementing stress testing.Reliance on models for credit riskmanagement as under Basel IInecessitates protective measuresagainst the pitfalls of model risk andunexpected developments. In thiscontext, this document then consid-ers stress testing as an indispensableingredient of a sound risk manage-ment concept from both a regulatoryand business point of view.

• Credit pricing. One of the far-reach-ing promises related to the introduc-tion of Basel II–compliant ratingsystems was that they could providethe foundation for risk-adjusted pric-ing and value-based management inthe credit business. Inspired by thedevelopment of the markets forcredit risk transfer, we shed a criticallight on this tenet and consider newapproaches to credit pricing.

• Improving credit processes. Animportant concept underlying BaselII is the requirement that risk modelsused for regulatory purposes shouldalso be embedded in the credit riskmanagement processes. This docu-

ment takes this idea several stepsfurther and explains how the creditprocesses can be changed andtuned to value creation by using theBasel II data and by combining therisk results with advanced customervalue metrics in the retail and small-business segments of the creditbusiness.

• Managing the credit portfolio. Ifcustomer value management is anoverriding goal of retail credit, thenactive credit portfolio managementhas a similar role in the corporatecredit sector. The realization of thisconcept requires both the changes inthe markets for the transfer of creditrisk and the advances in riskmethodology fostered by Basel II.This document explains how activecredit portfolio management can beimplemented to reap the benefits ofboth developments for value-basedcredit risk management.

• Dealing with non- or sub-perform-

ing loans. The two final sections ofthis publication focus on themanagement of non- or sub-perform-ing loans—first, with a look at theintricacies introduced into debtrestructurings when credit deriva-tives are involved and, finally, with adiscussion of how Basel II data andmethodology can be used to imple-ment a bad-debt managementprogram that is guided by value-based objectives.

In retrospect, the implementation ofBasel II, together with the develop-ments in the markets for credit risktransfer, will be considered a turningpoint in value-based credit riskmanagement. Investments in systems,processes, data, and expertise haveresulted in a platform of measurementinfrastructure and understanding thatcan now be fully utilized.

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One of the most importantefforts in the implementa-tion of Basel II was toincrease the amount andquality of available data oncredit exposures. Thisimmense investmentshould now be leveraged toserve the business beyondpure regulatory compliance.

Tim Schabert (Germany), Peter Lam(Australia), and Marco Lenhardt(Germany) explain how sound datagovernance can lay the foundations forcompetitive advantage through high-quality data.

Data quality refers to correct,complete, and timely information thatis available for a specific analyticaluse. Data quality is not an end in itselfnor can it be measured on an absolutescale; rather, it is subject to continu-ous change and new challenges.Implementation of Basel II increasedbanks’ data quality requirements.

Laying the FoundationsSound Data Governance and Data Quality as Asset andCompetitive Advantage

Concerning the group-wide consis-tency and degree of detail of data onthe customer level, the followingexamples are worth noting. First,before the advent of Basel II, maintain-ing in the system the correct coding ofthe group affiliation of a business part-ner’s foreign subsidiary was of impor-tance only in the context ofconcentration risk management, andtherefore only for large exposures.With Basel II, having such a code is aprerequisite for the rating process ofcredit customers, and thus relevant toall business partners irrespective ofexposure size. Second, under Basel II,when processing guaranties receivedas collateral, detailed business-partnerinformation on the provider of thecollateral is necessary, whereas in thepast a bank obtaining a guarantee fromanother bank had to indicate only thatthe latter, as the provider of the collat-eral, was based in an Organisation for Economic Co-operation andDevelopment [OECD] zone A country.

However, compliance with regulationsis not the sole driver for increasingrequirements on data quality and avail-

ability. Indeed, such requirements willcontinue to increase in keeping withthe performance-driven developmentsin the financial services industry.

The progressive integration of risk andperformance management requiresincreasing data compatibility withinthese two financial function disciplines,which have until now been largelyseparate. Consider the examples of a“credit treasury” and a performance-oriented management of risk capital.They both require risk and investmentreturn information on credit positions atthe same level of granularity. This infor-mation is generated in the process oforigination; thus, the efficiency of aninstitute’s operations on the secondarycredit market is closely tied to the avail-ability and quality of data that have theirorigin in the activities on the primarycredit market. This dual purpose givesrise to new demands on the quality andavailability of these data. For example,by keeping side-letter agreements onpaper files only, as is the common prac-tice for corporate loans, risk-relevantinformation may not be available forcredit portfolio management.

Managing Credit Risk: Beyond Basel II 4

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Data deficiencies increase transactioncosts and underscore the importanceof data quality as a factor in achievingcompetitive advantage. Whether trig-gered by regulatory requirements or bycompetitive market pressure, dataquality is an asset that influences aninstitute’s ability to reach its targets.Consequently, measures that aim atimproving data quality represent invest-ments that can be justified by theircontribution to the achievement ofperformance goals.

The Case for DataGovernanceAchieving efficient and long-termimprovement in data quality requiresdata governance that is adjusted toidentified analytical purposes and qual-ity goals. Data governance describesthe set of group-wide regulations andstructures referring to the supply andprocessing of data. As discussedbelow, data governance encompassesthree mutually interacting dimensions:IT architecture, organization, andprocesses.

IT Architecture

A consistently high level of data qual-ity depends on the support of suitableIT tools. Quality deficits that resultfrom inadvertent inaccuracy of datainputs or automated data processes(e.g., migrations) cannot be remediedby organizational and process-relatedmeasures alone.

Beyond a certain data volume, asystematic detection and correction of deficient data, data inconsistencies,or double entries can be performedefficiently only with the help of auto-mated analysis routines, which require a centralized data household to operate effectively. Key elements of an adequate IT architecture includeseparation of analytical data layer fromoperational data repository, modulariza-tion of functions, and elimination ofredundancies (Figure 4).

Organization

The complexity of data managementprocesses in a bank increases with thenumber of local divisions that producerelevant information and the number ofusers requiring reliable data for diverseanalytical purposes. Beyond a certainlevel of complexity, data quality can no

5 Managing Credit Risk: Beyond Basel II

longer be regarded as a by-product ofexisting data processing routines.Without a proper data managementframework, quality assurance meas-ures may easily account for 75 percentor more of the time available to gener-ate a report.

One option to raise the efficiency ofdecision support processes is to indus-trialize data supply operations and toseparate data analysis processes fromany quality assurance activity. A centralunit would be responsible for ensuringa high level of data quality and forproviding all analytical processes withreliable data.

An existing IT architecture largelydetermines the range of potential orga-nizational structures for data gover-nance. For example, centralizedresponsibility for the functional valida-tion of input data is possible only withthe consolidation of decentralized indi-vidual databases in a central data ware-house. Thus the existing IT architectureand the road map of its further devel-opment determine the scope ofoptions for the definition of a specificdata governance model.

Processes

Efficient and effective processes arethe most important ingredients forachieving a high level of sustainabledata quality. Although data is oftencaptured bottom-up, the control frame-

work to ensure that the bank canachieve high standards of data qualitymust be driven from the top. Seniormanagement needs to build aware-ness for the importance of data qualityin all areas of the organization. A criti-cal step is to develop key performanceindicators (KPIs) for data quality and toreengineer data ownership to ensureclear accountability among the busi-ness units for the quality of data theycapture. The KPIs should be linked totangible values, such as performanceevaluation for year-end bonuses, orpenalties, such as ascribing a highercapital charge to business units wherethese KPIs have not been met.Effective KPIs should be specific,measurable, business oriented, control-lable, and reportable and involve theinspection of data.

General Principles of DataGovernanceWhile an explicit definition of datagovernance has to be adjusted to thespecific conditions prevailing in a finan-cial institution, the following guidelinesare applicable:

• Responsibility for data quality shouldbe resident in the business unitwhere the data are introduced intothe process and/or where the data(for example, by calculation) areproduced. The responsibility for thequality of business and customer

Figure 4: Integrated Accounting, Performance, and Risk IT Architecture

Source: KPMG in Germany, 2007

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data, for example, should be withthe respective market divisions.Accountability for the results datathen rests with the units responsiblefor the appropriate application of theanalytical methods and their correctimplementation. Ownership forresults data also encompassesresponsibility for their completeness:Business or customer informationthat was rejected during theprocessing—due, for example, toundiscovered quality faults—has tobe corrected and (as a quick fix) bereentered into the processing. Thecorrection of quality deficiencies ofinput data rests, again, with therespective market divisions.

• Downstream quality assurancemeasures, such as data validationprocedures outside the market divi-sions, should be pooled in an orga-nizationally separate central unit.This unit would be responsible forcontrolling the completeness,correctness, and timeliness of datadeliveries from the diverse busi-ness divisions and for providingvalidated data at least to the analyt-ical processes the results of whichhave to be compatible.

• All measures that aim at the provi-sion of a high level of data qualityshould be organized in the form ofquality “gates” that build upon eachother (Figure 5):– Quality gate 1. Technical process-

ability: Technical assessment ofsupplied data and employment oferror-handling routines

– Quality gate 2. Systematic valida-tion of data content: (1) Dataanalysis designed to detect miss-ing data, duplicates, and so forth(by a dedicated data quality team)using “intelligent” routines and (2) correction of detected qualitydeficiencies in source systemsand clearing of database

– Quality gate 3. Data analysis: (1) Detection of (sporadic) dataquality problems (such asoutliers) in the course of dataanalysis for decision support, (2) correction of detected qualitydeficiencies directly in therespective reports or analyses,and (3) triggering the data correc-tion in the source system

Managing Credit Risk: Beyond Basel II 6

The quality gates should be comple-mentary so that at the higher levels theonly errors occurring are those thatcould not be identified efficiently at anypreceding stage. The process of dataanalysis by highly skilled experts tosupport business decisions must not beconstrained by issues that should bedetected and resolved at earlier stages.

Thus, by supporting business decisionmaking and enhancing the accuracy ofbusiness decisions, efficient datagovernance becomes a strong compet-itive advantage.

Figure 5: Schematic View on Quality Gates

Source: KPMG in Germany, 2007

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Basel II and InternationalAccounting Standard (IAS)39 “Financial Instruments:Recognition and Measure-ment” both require banksto model and measure therisks from their creditengagements.

Frank Glormann (Germany) exploreshow Basel II investments can be lever-aged for IFRS purposes, especially onimpairment provisioning.

What is the right way to measurecredit risk? Depending on their respec-tive objectives, regulators and account-ing standards-setters have come upwith different answers. But from amanagement point of view, arriving atan integrated approach is imperativefor at least two reasons:

• Cost savings. Setting up and main-taining two methodologies requiringdistinct data for Basel II and IFRSloan-loss calculations would seem

In Search of the Right MeasureLinking Credit Risk Modeling and Measurement in Basel II and IFRS

7 Managing Credit Risk: Beyond Basel II

unsustainable. Fortunately, underly-ing Basel II models and data alsomay be used for IFRS purposes.Bridging the gap between the twosets of rules, however, still requiresconsiderable and costly modifica-tions and supplementary analyses—but the costs for implementing IFRSloan-loss calculations from scratchwould be considerably higher.

• Consistency of risk reporting. Bankmanagement relies increasingly onan integrated view of the institution’sregulatory, accounting, and businessoperations. Achieving such integra-tion requires coherent and consis-tent reporting across all threeperspectives. Consequently, dataand methodologies must be aligned,and the transition between thedifferent sets of calculations must bewell understood.

Developing an integrated approach forloan-loss calculations under IFRS andBasel II requires consideration of thefollowing:

• IFRS requirements on loan-lossprovisioning

• Similarities and differences betweenIFRS and Basel II

• Applying Basel II models for IFRSimpairment calculation

IFRS Requirements onLoan-Loss ProvisioningIFRS loan-loss provisioning can occuron an individual or on a collective (port-folio) basis. Specific analysis of signifi-cant and impaired assets is necessaryto calculate the so-called “specific”loan-loss provision. All the otherassets, such as individually not-signifi-cant loans and individually significantbut non-impaired loans, are subject toa portfolio approach to loan-loss provi-sioning and thus are potential candi-dates for the application of Basel IImethodologies (Figure 6 on page 8).

Similarities and DifferencesBetween Basel II and IFRS Both Basel II and IFRS agree, inessence, in their international focusand their general goal to providemarket participants and supervisoryauthorities with transparent and

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precise information. Consequently,many of the requirements and sourcesof data are similar under IFRS andBasel II.

Supplementary IAS 39.AG92 recog-nizes the considerable synergiesbetween IFRS and Basel II by acknowl-edging that, ”formula-based approachesor statistical methods may be used todetermine impairment losses in agroup of financial assets” (as illus-trated in Figure 6). However, there aresome conceptual differences betweenIFRS and Basel that prevent banksfrom using unadjusted Basel II dataand models for IFRS purposes.

The most important of these differ-ences are described below:

• General purpose. The overall Basel IIobjective is to ensure reliability andstability of the financial system. Thus,it requires banks to hold an adequatelevel of funds against the expectedrisks they take plus a buffer againstunexpected risks over the course ofthe following 12 months. By contrast,the IFRS perspective is to providerelevant information for decisionmaking at a defined point in time,specifically the balance-sheet date.

• Incurred loss versus expected loss.Whereas the objective of IFRS is toensure that financial statementsadequately reflect losses incurred as

Managing Credit Risk: Beyond Basel II 8

of a balance-sheet date, Basel IIfocuses on expected and unex-pected losses. According to IFRS, afinancial asset is impaired, andimpairment losses are incurred, ifobjective evidence exists indicatingimpairment as a result of a pastevent that occurred subsequent tothe initial recognition of assets.Incurred losses are based oneconomic/market events or riskcircumstances (risk conditions/impairment trigger) that haveoccurred after the initial recognitionof the asset and provide evidence ofa forthcoming default affecting futurecash flows that may be reliably esti-mated. IAS 39 explicitly states thatexpected losses as a result of futureevents, “no matter how likely,” arenot recognized.

• Cycle dependency. IAS 39.59(f)(ii)states that, “an adverse change innational or local economic condi-tions…should be used as a basis fordetermining that there is a measura-ble decrease in their estimated cashflows.” In other words, economictriggers are allowed as an indicationthat impairment may be present in agroup of assets so that, conse-quently, IFRS seeks to reflecteconomic volatility in provisionlevels. Although Basel II is notexplicit on this topic, in the discus-sions around cyclicality and subse-

quent potential credit crunchesduring development, Basel II indi-cated that neither regulators norbanks wish to see erratic move-ments in capital requirement levelsdue to changes in economic condi-tions. A reasonable strategy underBasel II therefore seeks to reducevolatility in the level of capital require-ments over the economic cycle.

• Loss definition. Technically speak-ing, Basel II defines a loss event asoccurring when either or both of twoconditions are met: (1) The obligor isunlikely to pay its credit obligationsor (2) the obligor is 90 days past dueon any material obligation. In otherwords, loss in the sense of Basel IImeans some form of economic lossand the expected loss calculationrefers to the whole asset ledger.Under IFRS, an impairment loss isdefined as occurring when thedifference between the presentvalue of the expected cash flows,discounted at the effective interestrate, and the carrying value of theloan becomes negative.

This difference in loss definitionsbetween Basel II and IFRS impliesthat the starting basis for Basel IIexpected loss (EL) calculations andIFRS impairment calculations arealso different—because, for exam-ple, non-cash transactions such as

Figure 6: Loans and Receivables

Source: KPMG in Germany, 2007

NOTE: Above-mentioned are applicable for only on-balance-sheet items

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indirect costs for the overhead of acollection department or latepayment charges are consideredunder Basel II but not under IFRS.Also, the Basel II loss given default(LGD) number requires the inclusionof capital costs whereas IFRS explic-itly states that the discount rate tobe used is the same “effective inter-est rate” used to recognize incomeon the asset before it was impaired.

Applying Basel II Models forIFRS Impairment Calculation The basic equation for calculatingincurred loss (IL) under IFRS looksquite similar to the Basel II formulae,as in Figure 7.

Discussed below are the specificcomponents of the equation andrelated detail on Basel II characteris-tics, IFRS requirements, and adjust-ments required.

Assignment of Portfolio

• Basel II: Expected Loss is deter-mined with respect to the wholeasset ledger

• IFRS: Calculation of loan loss provi-sions required for specific assets; forexample:– (i) Financial assets classified as

Loans and Receivables (LaR)carried at amortized costs

– (ii) Off balance-sheet itemsaccording to IAS 37

– (iii) Fixed income assets classifiedas available for sale (AfS)

• Comparison: The Basel II methodol-ogy can be leveraged in the abovecases (i) and (ii). However, in thecase of fixed income assets classi-fied as available for sale (AfS), thereis an inevitable asymmetry betweenBasel II and IFRS. These financialinstruments, which are held on thebanking book, are not subject toportfolio or general loan loss provi-sioning IFRS. Hence, Basel II param-eters do not play a role in their IFRStreatment. They may still-—and typi-cally will be—-subject to the IRBapproach under Basel II just like ordinary loans and receivables.

• Adjustments: Apply Basel II modelsto relevant IFRS portfolio and takeinto account that portfolio variationsmight require changes in Basel II

9 Managing Credit Risk: Beyond Basel II

parameters since changes in portfo-lio composition alter the basis forparameter estimation.

Adjustment concerning the expo-

sure at default (EAD)

• Basel II: Capital requirement calcula-tions are done using EAD numbers,i.e., the expected exposure atdefault, including unused creditlimits, projected over a one-year horizon.

• IFRS: The basis for loan loss calcula-tions under IAS 39 is the IFRS bookvalue, not the Basel II EAD (IFRSbook value < EAD). Losses expectedas a result of future events, nomatter how likely, are not recog-nized. Unused credit limits are notincluded, and book values are usedas of the balance-sheet date.

• Adjustments: Substitute EADnumber with IFRS book value as ofthe balance-sheet date for IAS 39loan loss provisioning.

Adjustment concerning the proba-

bility of default

• Basel II: The PD defines the probabil-ity of a customer (transaction viewalso permitted in the retail segment)to hit one of the Basel II defaultevents over a one-year time horizon.A PD is determined for any borrowerand for any credit transaction in theretail business by the use of internalrating systems.

• IFRS: The methodology of applyinginternal rating systems for Basel IIpurposes is also valid for determin-ing IFRS PD, with appropriateadjustments.

• Adjustments: Delineate Basel IIprediction horizon from a one-yearforward-looking PD toward thebalance-sheet incurred-loss point ofview as required by IFRS.Additionally, the need for adjustingthe cyclical behavior of the underly-ing PD models might be verifiedsince Basel II models often use ahybrid calibration instead of a purepoint-in-time perspective, as favoredby IFRS (IAS 39.59(f)(ii)).

Adjustment concerning loss given

default

• Basel II: LGD numbers are based onthe definition of economic lossesrelating to the expected amount ofdebt at the time of default, for whichfuture cash flows are discountedusing a risk-free market rate.

• IFRS: The reference figure for deter-mining impairment is the IFRS bookvalue as of the balance-sheet dateusing the effective interest rate todiscount expected cash flows.

• Adjustments: Adjust the Basel IILGD numbers in the following way:– Base LGD calculations on the

carrying amount instead of the LGD.

Figure 7: Application of Basel II Parameter for IFRS Provisioning

Source: KPMG in Germany, 2007

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– Take all of the collateral into account.– Use effective interest rates instead of risk-free rates to discount future

cash flows.– Neutralize the Basel II downturn LGD effect.

Adjustment of expected loss to incurred loss concept

• Basel II: “Plain vanilla” EL models are used.• IFRS: Consider credit losses incurred at the time of the impairment loss

calculation.– If losses already have been identified, then impairment losses need to be

calculated on an individual level in case of significant financial assets or on aportfolio level, assuming a PD of one.

– If losses have not been identified so far (an “incurred but not reportedloss”), apply the EL based loan loss calculation on a portfolio level.

• Adjustments: Introduce the loss identification period (LIP) concept to managethe transition from expected to incurred losses. The LIP is the time periodbetween the occurrence of a specific impairment event and objective evidenceof impairment becoming apparent on an individual basis—that is, the timebetween the loss event and the date an entity identified its occurrence. TheLIP can be incorporated into the expected loss equation via the LIP-factorcalculated as:

Given this, the IL can be calculated as:

The LIP concept is illustrated in Figure 8.

ConclusionBasel II models can serve as a starting point to calculate loan loss provisions underIFRS—but specific adjustments are needed to transform Basel II expected lossnumbers into incurred loss amounts required by IFRS. Consequently, leading banksare currently implementing detailed and complete IFRS-compliant approaches forloan-loss provisioning using synergies with Basel II models and results.

Managing Credit Risk: Beyond Basel II 10

Source: KPMG in Germany, 2007

Figure 8: Loss Models

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Under Basel II Pillar 1, newquantitative measures forcredit risk, such as proba-bility of default, loss givendefault, and exposure atdefault, have been intro-duced on a large scale.Similarly, under Basel IIPillar 2, the concepts ofrisk-bearing capacity,economic capital, riskcorrelations, and concen-trations have gainedincreased attention fromrisk managers and regula-tors alike.

Ralf Hennig (Germany) and FrancescoMerlin (Italy) explain how these riskmeasures and concepts can be usedto create risk-sensitive credit limitsystems going beyond the traditionalgross exposure limits.

The purpose of a credit risk limitsystem is to ensure that a bank’sactual risk-taking is in line with its risk-bearing capacity. A particular focus ison the avoidance of excessive riskconcentrations, which may jeopardizethe existence of a bank. With thisobjective in mind, a limit system needsto be consistent across all parts of anorganization to ensure that a bank’srisk-bearing capacity is not exceeded atthe aggregate level.

This goal implies that the actual size ofthe limits must be derived from thebank’s risk appetite, business objec-tives, and risk-bearing capacity. Thisprocess is often guided by qualitativeconsiderations; however, quantitativetechniques are also being applied,including the definition and quantifica-tion of stress scenarios against whichthe bank aims to protect itself by creditrisk limitation techniques.

Going to the LimitOn the Use of Advanced Credit Risk Measures in a CreditRisk Limit System

11 Managing Credit Risk: Beyond Basel II

As discussed below, three dimensionsneed to be considered in designing acredit limit system that serves thispurpose:

• The risk metrics used to measurethe credit risk

• The different objects of limitationtogether with the specific reasonsfor their limitation

• The way the limit system is embed-ded in the organization and businessprocesses

The Metrics for MeasuringCredit RiskWith the improvements that Basel IIhas brought about for the quantifica-tion of credit risk at the individual andportfolio levels, a number of metricscan now be used to measure creditrisk (as depicted in Figure 9).

The characteristics defined in Figure 9can be classified along two dimen-sions: (1) the extent to which a metric

Figure 9: Characteristics of Different Credit Risk Measures

KPMG in Italy, 2007

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is risk sensitive and (2) the extent towhich it is capable of taking worst-caselosses correctly into account.

As shown in Figure 9, traditional creditrisk metrics, such as gross or net expo-sure, are less risk sensitive than othersbecause they do not change with thecredit quality of the obligor, but ratherfocus strongly on worst-case lossessuch as the default of a single largeobligor or a sovereign transfer riskevent. By contrast, statistical riskmetrics—such as expected loss, regu-latory capital (in particular in the caseof the internal ratings–based approach),or even economic capital—are muchmore risk sensitive in that their valueschange with credit quality and, in thecase of economic capital, credit riskconcentrations, but fail to captureworst-case scenarios.

Also, statistical risk measures are muchmore subject to model risk than expo-sure-based metrics, but they oftenpermit a more meaningful risk aggrega-tion across, for example, products,obligors, or business lines than theirexposure counterparts. The classicexample of credit lines that are diffi-cult to aggregate under normal condi-tions is the case of counterparty risklimits and traditional credit lines for asingle obligor.

These considerations show that nosingle risk metric is a panacea. Rather,the question of which to use dependson which risk metric best fits thepurpose at hand.

Credit Risk LimitationScenariosTable 1 links entities and relatedreasons for credit risk limitation withpotential risk metrics.

Depending on the object and therelated reason for the limitation, acombination of a “worst-case” metricand a more risk-sensitive metric can beappropriate. For example, consider thecase of a large exposure with a veryhigh credit quality obligor. Although insuch a case the economic capitalmetrics would rule it out as a majorsource of risk, a net exposure limitmight kick in and protect the bankagainst lending excessively to an entitythat at some future point may poseserious risk.

Managing Credit Risk: Beyond Basel II 12

Conversely, a net exposure metric mayfail to pick up risk concentrations fromexposures of medium size but lowercredit quality—which are jointly morevulnerable to adverse economic condi-tions—or from exposures that exhibitother common risk characteristics.Such issues are more easily recognizedas risky by an economic capital metric.

Another important consideration inchoosing a risk metric for a credit risklimit system is the availability of up-to-date risk figures, especially with regardto economic capital. Proxies may berequired to obtain an estimate of thetrue economic capital figure betweenproper calculations.

Most important, the risk limit systemmust be understood by client relation-ship managers and credit risk managersalike. From this point of view, there areadvantages for simple exposure limits.Therefore, tools that translate morecomplicated risk-sensitive credit limitsinto traditional exposure limits arerequired. Users should acknowledgethat, in contrast to fixed exposurelimits, the “exposure limits” resultingfrom such a translation are liable tochange along with changes in the char-acteristics of the transaction, theobligor, or economic conditions.

Entity Possible Characteristics

Reason of Limitation

Possible Risk Metric

Single Obligors

Economic-risk enti-ties in terms of affili-ated group

Highest correlationbetween nameswithin oneeconomic riskentity in terms ofaffiliated group

Net exposure,contribution toeconomic capital

Largest GroupExposures

Sum of largestsingle obligors (e.g.,largest five obligors)

Portfolio with lesstendency towardsconcentration infew single obligors

Net exposure

Countries All sovereigns andprivately held enti-ties in the country

Highest correlationin case of sovereigndefault and transferevents for all enti-ties in the country

Net exposure

Regions Group of countriesin a geographicregion

Economic depend-ence betweencountries in aregion

Economic capital

Industries Industry segmentswith high defaultcorrelation

Correlation betweensingle addresses inthe private sectorwithin economiccycle of the industry

Economic capital

RatingClasses

Single rating classesor groups of ratingclasses

Determination ofdesired portfoliocomposition acrossrisk classes

Expected loss

“Hot spots” Risky portfoliosubsets that aredefined by riskmanagement basedon general riskpolicy

Striking concentra-tion in “hot spot”and risk politicalconsiderations

For example netexposure,expected loss oreconomic capital,conditional ondependence ofobligors within“hot spot”

Table 1: Limitation Dimensions

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Finally, not all of the above objects of limitation will or should play the same rolein a credit limit system. While a combination of exposure and economic capitallimits may prove useful on the single-obligor level, other limits such as industryor rating class limits will be considered only at the level of the whole portfolio.Rather than being perceived as “strict limits,” the latter may be used as softerguidelines along which to structure the credit portfolio. Also, in practice, one mayuse a smaller or different choice of limit objects than those offered in the table.

Embedding the Limit System in the Organization andBusiness ProcessesWhile comprehensibility of the limits system is a prerequisite for embedding it inthe processes of a bank, the key issue arising in this context is described inFigure 10.

Recall that the purpose of a credit risk limit system is to ensure that a bank’soverall risk-taking is in line with its overall risk-bearing capacity. Figure 10 illus-trates the interaction of the organizational structure (represented by the horizon-tal view) and the risk limits, which extend across the business areas. Thisinteraction creates a bank-wide risk view that management can use to protectthe bank against undesired risk concentrations.

Enabling decentralized decision making while maintaining a bank-wide view ofthe risk creates challenges. At first glance, an approach would be to break downthe limits to the different business units, giving rise to a matrix structure similarto the one in Figure 10. This approach would, however, be highly inflexible, andthe frequent result in practice would be (1) the implementation of a complicatedsystem of limit transfers and limit lending or (2) the allocation of wide limits tothe business units to contain interference with operations. The latter approach,however, often leads to bank-level limits that are so high as to be totally out ofproportion with the bank’s risk-bearing capacity and are only tolerated with thetacit understanding that they will not all be used at the same time.

13 Managing Credit Risk: Beyond Basel II

As a result, a credit risk limitationsystem with material risk managementimpact may create substantialconflicts. These conflicts not only arisebetween business units and credit riskmanagement, which may inhibit busi-ness due to the limitation, but alsoemerge as different business unitscompete for the finite risk-takingcapacity of the bank.

Appropriate governance structures andprocesses can eliminate a large part ofthe conflict potential. Useful elementsof such a structure would be:

• A credit strategy and guidelines inaccordance with overall credit portfolio objectives

• A limit reservation concept• An escalation procedure in cases of

scarce risk-bearing capacity.

It may, however, be even more promis-ing to join the pure risk view conveyedby a credit limit system with a valueperspective. This combined view maybe introduced very effectively throughan active credit portfolio managementunit that keeps the bank-wide creditrisk under control while interactingflexibly with the business units, basedon the principles of value-basedmanagement and risk-adjusted pricing.

These ideas will be explored further in the section, “An OrchestratedApproach to Value Creation.”

Figure 10: Linking Credit Limit System to Bank Organization

Source: KPMG in Germany, 2007

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The inclusion of stress test-ing requirements within theBasel II framework is anopportunity for institutionsto integrate their quantita-tive and qualitative methodsand to reduce the modelrisk that arises.

As Steven Hall (United Kingdom),Francesco Merlin (Italy), and KristianTomasini (Italy) explain, the require-ments drive banks to explore issues ofunexpected loss, economic capital, andregulatory capital more widely than inthe past.

Exploring the UnexpectedDeveloping and Using Credit Risk Stress Testing in Risk Management

“Stress testing is a risk managementtechnique used to evaluate the poten-tial effects on an institution’s financialcondition of a specific event and/ormovement in a set of financial vari-ables. The traditional focus of stresstesting relates to exceptional but plau-sible events”1

To understand the general approachtoward stress testing, it is important tounderstand the following key aspects:

• The environment in which bankshave to operate (considering therange of exceptional but plausibleevents)

• The definition of unexpected loss(within the formal risk managementsphere)

• How to use the outcome of thestress test

Once these issues are clarified,management can consider the param-eters to be stressed and the types ofstress tests to be considered.

The EnvironmentTo define how stress tests could affectan organization, management shouldconsider the types of stress the bankencounters and the environment inwhich it operates. Essentially there canbe two sources of stress: (1) purelyidiosyncratic events such as the defaultof a large obligor and (2) developmentsaffecting the entire market, such as aneconomic downturn. A large default oradverse market developments such asa mild recession may affect one bankmore severely than another, dependingon the aggressiveness of its lendingstrategy or business model.

Managing Credit Risk: Beyond Basel II 14

1 Bank for International Settlements, Committee of the global financial system, January 2005: Stresstesting by large financial institutions: survey results and practice.

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As a consequence consider the following:

• Stress tests are a useful exercise to(1) help an institution identify plausi-ble but extreme combinations ofidiosyncratic and market events thatcould endanger the stability of theinstitution and (2) define potentialstrategies and policies as a stand-alone response to those events. Thebank should set its internal level ofcapital with the intention of meetingsuch events and based on its currentenvironment and status.

• Extreme systemic catastrophicevents have to be managed in acoordinated way by the appropriatesupervisory authorities within thefinancial system. Thus, a commonframework for stress testing and itsthresholds should be defined at theinternational level, whereupon indi-vidual institutions would calibrateand refine this framework based ontheir own circumstances.

Unexpected LossUnexpected loss (UL) could be definedas the difference between expectedloss (EL) and the value at risk (VaR) ata given confidence level, or theexpected shortfall exceeding the VaR.One problem with this approach is thatsuch a loss might be not only unex-pected but also quite unrealistic, asthis definition is purely statistical innature. Therefore, stress tests can beuseful either to determine which unex-pected losses are plausible or toprovide information to help determinethe level of economic capital to be heldby the institution.

“Reading” a Stress Test

When integrating the informationobtained from a stress test into theinstitution’s management framework,management should consider how touse it in an active way rather than as astandalone regulatory compliancerequirement2.

Therefore, it could be useful for theinstitution to introduce indicators andthresholds to help determine when to:

• Inform management about potentialcritical developments

• Constrain the expansion of the insti-tution’s business into a risky area

15 Managing Credit Risk: Beyond Basel II

• Reduce the risk of a specific sub-portfolio with the help of securitiza-tion or syndication

• Readjust the threshold for the limitsystem.

In general, the indicators should bethose that the risk management func-tions already consider to understandthe portfolio’s risk profile. Indicatorscould include EL, UL, expected short-fall, increase in capital charge(economic and/or regulatory), solvencyratio, or risk performance measure-ment indicators.

Credit Risk Stress Testing:The Risk ParametersOnce it has defined the boundaries ofthe stress test, the meaning of UL,and how to use the stress test out-puts, a bank can identify risk parame-ters for the credit risk stress test. Thefirst step is to distinguish between astress test and (1) available risk meas-ures for regulatory capital and (2) riskparameters for economic capital.

For regulatory capital the well-knownrisk parameters are the probability ofdefault (PD), the loss given default(LGD), and the exposure at default(EAD). PD is by far the most popularrisk parameter for stress, and tests areoften done either through the ratingassignment or by direct modification of PD.

In the first method, the change ineconomic conditions causes a variationof the inputs used to calculate therating, and this variation leads to achange in the rating assignment. Thus,a possible stress scenario could bedetermined after identifying the riskdrivers that produce those changes andrelative impact. Another scenario couldbe obtained by simply “shocking” therating class distribution for part of aportfolio without defining a parametricframework that links the shifts tospecific risk factors. In general, a stresstest where the assignment of theobligor rating grades is altered has theadvantage of allowing for the inclusionof transitions to nonperforming loans.

2 The management body has ultimate responsibility for the overall stress-testing framework.

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In the second method, the direct modi-fication of the PDs for rating gradescould have its origin in the influence ofsystematic and idiosyncratic riskfactors that could be estimatedthrough historical statistical analysis.This approach could be too complex tobe implemented in the first stage.Therefore, another possibility to modifythe PDs directly could be derived fromanalysis of historical rating transitionsin periods of economic stress. Ingeneral, the advantage of modifyingthe PDs directly is in the great varietyof changes that can be made; the maindisadvantage is that there is no changein the assignment to performing andnonperforming loans and this processhas to take place at the start of the PDmodification process.

The other risk measures, LGD andEAD, are by definition already condi-tioned to the situation where theobligor is in default. In addition, theestimation of LGD and EAD must beappropriate for an economic downturn.Consequently, the ability to stressthese quantities is restricted to:

• Exogenous factors such as exchangerates for EAD or collateral values forLGD

• Risk drivers that could influence allthe risk parameters (that is, a riskdriver that affects the collateral valueat the same time as the PDs).

To estimate economic capital, the riskparameters PD, LGD, and EAD mightnot be sufficient to design appropriatestress tests. In addition to the parame-ters described above for regulatorycapital, one has to take into accountthe building blocks for the credit riskportfolio model (such as correlationbetween loans, correlation betweenrisk measures, concentration, and soforth). Therefore, to define a methodfor stress testing economic capital, thefirst step is to analyze the bank’sspecific portfolio model, consider itscomponents, stress them; and try tocover situations where the model maylikely fail to indicate the risk properlydue to known and accepted shortcutsin the modeling.

Managing Credit Risk: Beyond Basel II 16

Types of Stress TestsAfter defining the institution’s environ-ment, the meaning of unexpected loss,and the purpose of the stress test(regulatory capital versus economiccapital), it is useful to consider the twomajor approaches to stress testing:

• Constant stress test: this involvesthe (simultaneous) movement of riskcomponent(s), without taking intoaccount the properties of the sub-portfolios or the interrelations withexternal events (that is, a flat stressis applied for PDs).

• Model-based stress test: thisapproach could be divided intounivariate (relying on one randomvariable) and multivariate (involving anumber of independent mathematicalor statistical variables) approaches.These methods attempt to explorethe relationship between risk drivers(e.g., macroeconomic variables) and the impact on risk parametersthrough econometric models. Thisapproach also considers the potentialportfolio effect, allowing for thepossibility that a single risk drivercould have a positive impact on onespecific sub-portfolio and a negativeimpact on another (e.g., an increasein energy prices could have a nega-tive impact on the retail portfolio but a positive impact on the corpo-rate lending portfolio focused onenergy companies). Importantly, italso considers the influence of theidentified risk factors on different risk types.

Both approaches depend in some wayon the definition of the scenarios, whichcould be specified in three ways:

• Historical scenarios: extremeuniverses of risk factors observed inthe past that are related, largely, tohistorical events and crises. They areapplied to the current situation andportfolio.

• Statistically based scenarios:based on the joint statistical distribu-tion of risk factors. The key challengein this approach is to have enoughhistorical data to define the distribu-tion. The great advantage of this

approach is that sufficient dataenables one to associate a specificprobability with the scenario.

• Hypothetical scenarios: focuses onrare events that might have animportant impact on the portfolio buthave not been observed yet.

With respect to the design of stressscenarios, approaches can be either:

• Portfolio or bottom-up: the identifi-cation of risk drivers is strictlydependent on the portfolio composi-tion (for example, relevant drivers fora bank focusing on real estate wouldbe GDP, employment rate, and infla-tion rate rather than oil price,exchange rate, and so forth)

• Event or top-down: a study of theimpact of a chosen scenario (forexample, the terrorist attack ofSeptember 11, 2001).

ConclusionAs banks’ portfolios and activitiesbecome increasingly complex andfinancial and economic circumstancescontinue to change, the use of stresstesting to understand the impact ofportfolio and individual events becomesever more important. Increasingly,regulators are turning to stress testingas a way of understanding how compa-nies will respond in given situations.Senior management has a key role inestablishing a robust and comprehen-sive stress-testing framework.

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Under the so-called “use test,” Basel II requires banksapplying the internal ratings–based approach to use theirinternal ratings as an essential part of their credit riskmanagement processes3 and, hence, of the pricing ofcredit4. As a consequence, many in the industryexpected IRB banks to review their credit pricingmethodology to reflect the “true” risk-adjusted cost ofcredit as estimated by the risk parameters derived fromtheir Basel II rating systems.

This task may be much more challenging than suggested by current cost-basedapproaches to credit pricing, as Bernd Granitza (Germany), Maxine Nelson(United Kingdom), and Daniel Sommer (Germany) contend.

Pricing at All CostsComments on the Credit Pricing Today and Its Post-Basel II Future

17 Managing Credit Risk: Beyond Basel II

Prior to Basel II many in the industryfelt that bank credit was too cheap,meaning that banks were not earningcredit spreads sufficient to cover thecosts—in particular, the true economiccosts of risk and capital—for the loansthey were granting. With the wide-spread introduction of calibrated proba-bility of default (PD) rating systems,Basel II laid the foundation to obtainlarge-scale quantitative evidence tosubstantiate this uneasy perception ofcheap credit. Accordingly, “risk-adjusted” credit pricing was expectedto play a much bigger role in thepost–Basel II era5. It is probably tooearly to assess whether this predictionhas materialized, particularly as theeconomy has moved into a benigncredit environment since 2002.

Nevertheless, an almost unanimousview has emerged on the elementsthat a state-of-the-art, risk-based,credit-pricing scheme should contain.Banks using such an approach typicallyconsider four major components:

• Their own refinancing costs• “Standard” risk costs as expressed

by the expected loss• Administrative or operating costs• Capital costs6

In an IRB bank, expected losses aretypically calculated using the PDs asso-ciated with the respective internalrating grades. The fact that Basel IIPDs cover only a one-year time horizonis often dealt with by introducing ratingtransition matrices.7 These containhistorical probabilities of obligorsmigrating from a given rating grade toanother rating grade within one yearfrom the rating date. By iterativelyapplying these matrices to the originalone-year PDs, cumulative PDs forlonger time horizons are calculated.These are then used to determineexpected losses for longer-term loans.

3 International Convergence of Capital Measurement and Capital Standards (referred to as Basel II in thesequel) paragraph 444

4 See, for example, the German regulator’s MaRisk BTO 1.2 (6), which requires that there be a reason-able link between a bank’s risk assessment and its pricing of a loan.

5 As an example, see Jäger, Redak: “Austrian Banks’ Lending and Loan Pricing Strategies against theBackground of Basel II,” Austrian National Bank Financial Stability Report 12, pp. 92–103

6 As one source of evidence, see “Results from the survey of European banks carried out for the guideHow to deal with the new rating culture: A practical guide to loan financing for small and medium-sizedenterprises,” May 2005, European Commission

7 Alternatively, some banks use rating agency multi-year credit default frequencies associated with thePDs from their internal rating systems. This approach is subject to problems similar to those of thetransition matrix approach.

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Three questions arise as a result:

• Are Basel II PDs suitable for deter-mining expected losses in thecontext of cost-based loan pricing asoutlined above?8

• Is the cost-based approach suitablefor determining an economicallysensible risk-adjusted price of credit?

• How else could Basel II ratings beused in the context of credit pricing?

Suitability of Basel II PDsfor Determining ExpectedLosses for Cost-BasedCredit PricingBasel II requires that “a borrower ratingmust represent the bank’s assessmentof the borrower’s ability and willingnessto contractually perform despite adverseeconomic conditions or the occurrenceof unexpected events.”9 Moreover,Basel II stipulates that “PD estimatesmust be a long-run average of one-yeardefault rates for borrowers in thegrade…”10…and later explains thatthese may be taken from internaldefault experience, mappings to exter-nal agency ratings, or statistical defaultprediction models.11

Contrast these requirements with the fact that when pricing a loan abank must form the best possible estimate of the probability that anobligor will default before the contrac-tual repayment date, given all currentand past obligor- and economy-related information.

A long-run average of one-year defaultrates can only be an appropriate esti-mator for the default probabilityrequired in credit pricing if the ratingsystem is fully point-in-time, with theobligor re-rated regularly. By definition,such a rating system would reflectevery non-minor change in an obligor’sprobability of default—whether idio-syncratic or economy-related—by animmediate change of the rating

Managing Credit Risk: Beyond Basel II 18

assigned. If, however, the ratingsystem exhibited features of a through-the-cycle rating, which focuses on thestructural ability of an obligor to with-stand adverse economic conditions,using recent information about thegeneral state of the economy or theobligor’s industry to adjust the Basel IIPDs for pricing purposes would give amore accurate risk assessment.

The Basel Committee acknowledgesthat taking simple averages over pastdefault histories—as is often done incalibrating PDs—will be an effectivemeans of determining default probabili-ties per rating grade only if the ratingsystem is point-in-time.12

Moreover, whether a rating system ispoint-in-time or through-the-cycle willalso strongly affect the migration char-acteristics between rating grades.While rating migrations in a point-in-time rating system will be heavilydependent on the economic cycle, thisdependence is not an issue for athrough-the-cycle rating. This observa-tion has three important implications.

First, it is impossible to avoid explicitlytaking into account the effects of theeconomic cycle when estimating histor-ical PDs for pricing purposes. Eitherthey need to be considered in estimat-ing the one-year PDs in a through-the-cycle rating because PDs per ratinggrade fluctuate with the business cycleor they influence the estimation of thetransition matrix in a point-in-timerating.13 Either way, taking simple aver-ages of past default histories will notsuffice to obtain reliable estimates ofmulti-year PDs for pricing purposes.

Second, the technique of generatingmulti-year PDs by iteratively multiplyingthe one-year Basel II PDs by a ratingstransition matrix is highly questionable.To see why this is the case, suppose,for the sake of argument, one knewthe correct one-year PDs for each

obligor and the obligors in one ratinggrade all had exactly the same one-year PD. As previously stated, the tran-sition matrices for a point-in-time and athrough-the-cycle rating would be quitedifferent. Hence, iteratively multiplyinga transition matrix would yieldcompletely different multi-year PDs forthe point-in-time and through-the-cycleobligors. This paradox results becausethis technique of multiplying matricesignores the effects of the businesscycle on PDs and rating transitions.

Third, in a through-the-cycle ratingsystem migrations may be subject torating momentum. This means that afurther downgrade at a given ratingdate is more likely to occur if theobligor had already been downgradedat the preceding rating date. Someagency ratings have this property.14 Inthe presence of rating momentum, iter-ating on a transition matrix with dimen-sions confined to the rating grades willyield incorrect multi-period PDs.

As a preliminary result, unadjustedone-year Basel II PDs or multi-year PDscalculated using historical averages ofpast rating transitions would not besuitable for use in credit pricingmodels. Rather, it is necessary todetermine carefully to what extent agiven rating system exhibits point-in-time or through-the-cycle characteris-tics and whether the transitions aresubject to rating momentum. Theseproperties have to be taken intoaccount by making appropriate adjust-ments to Basel II PDs and transitionprobabilities. Such adjustments areexplicitly permitted by Basel II and donot contradict the requirements of theuse test.15

8 We will refrain from considering the LGD-component for the sake of simplicity.9 Basel II paragraph 41510 Basel II paragraph 44711 Basel II paragraph 46212 Basel Committee on Banking Supervision, Working Paper No. 14, Studies on the Validation of Internal

Rating Systems, February 2005, pp 18–2013 The latter would be even worse from a statistical point of view because it combines the already diffi-

cult task of estimating a full transition matrix with the problem that the observed rating transitions aredrawn from different distributions, which themselves depend on the business cycle.

14 Standard & Poor’s Annual 2006 Global Corporate Default Study And Rating Transitions15 Basel II, paragraph 444

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Suitability of the Cost-BasedApproach for DeterminingRisk-Adjusted Prices forCreditSuppose the previous issues regardingthe determination of suitable PDs hadbeen solved. Would one be welladvised to use a cost-based pricingsystem, as outlined in the introduction,for credit pricing? An important point toremember is that one of the funda-mental principles of modern asset pric-ing theory is that “risk-neutral”PDs—that is, PDs that already take themarket participants’ risk aversion intoaccount—should be used for pricingpurposes. Therefore, the key to auseful credit pricing formula is to findan economically sensible movementfrom historical to risk-neutral PDs.

In this cost-based approach, the twocomponents of funding costs and capi-tal costs could be that key. Certainly,when a bank’s securities—whetherdebt or equity—are traded in themarket, the expected excess returnsquoted provide a market assessmentof the risk in a bank’s portfolio ofinvestments and activities, and hencetransit from the historical to the risk-neutral PDs. However, when a bankhas multiple activities and holds alegacy credit portfolio that is largecompared with new loans, its expectedexcess returns on equity and debt arefar more reflective of the market’s riskassessment of its existing portfolio andactivities than of the market’s riskassessment of the new loans.

Thus, rather than providing a solutionto the issue of risk-adequate creditpricing, the only question that the cost-based approach to credit pricing cansafely answer is: At what price—otherthings being equal—is a given bankeconomically capable of taking acertain loan on its balance sheet?

While the answer is an important pieceof information for a bank’s controllingdepartment, it will not be a pricingstrategy that helps a bank to competesuccessfully in the credit business—

19 Managing Credit Risk: Beyond Basel II

particularly in a world of increasingpossibilities for credit (risk) transfer.

Uses of Basel II Ratings inCredit PricingNonetheless, Basel II ratings and asso-ciated PDs do have a role to play incredit pricing. If used to link themarket’s prices for credit risk to thefundamental credit quality of a givenloan, Basel II ratings provide an indis-pensable element in a credit-pricingmodel. To serve this purpose, theimportant point is not whether an inter-nal Basel II rating is more point-in-timeor through-the-cycle but whether astable relationship can be establishedwith ratings used in the credit markets.

A case in point is liquid credits whereBasel II ratings tend to be derived fromagency ratings by means of shadowrating methodologies. For these creditsthe market has learned to associatewhat are essentially through-the-cycleratings with obligors’ point-in-time,forward-looking risk assessmentsexpressed through their CDS (creditdefault swap) spreads.

Alternatively, recent rating agencyresearch looks at inferring agency-typeratings from CDS spreads.16 Resultsfrom such investigations may provehelpful in facilitating the movebetween a historical PD and a risk-neutral PD or—what amounts to thesame thing—provide a link betweenthe market price of credit risk and therespective agency rating class. Thebeauty of this approach is that thesemarket prices are frequently updatedand are available for a number of matu-rities without the need to resort totransition matrices.

As IRB banks have typically developedmappings between their internalmaster rating scales and agency ratingscales, one may try to extrapolatemarket prices for credit risk from theliquid credit market to the less liquidsegments of the corporate creditmarket. While crossing the borderbetween publicly rated and non-publiclyrated entities is never a trivial step, if

done carefully the resulting creditprices may be much more in sync withthe market and truly risk-adjusted pric-ing than the output of the cost-basedapproaches discussed previously.

In addition to mapping internal Basel IIratings to external agency ratings,banks may look toward rating method-ologies that agencies have developedfor analyzing SME-CLOs.17 Thesemodels are not a substitute for inter-nally developed rating models.However, a mapping may be estab-lished between a bank’s internal Basel II ratings and such agencymodels to enable a bank to gauge how its SME portfolios would behaveunder a potential CLO scenario. Theobjective is to infer information fromthe (primary) CLO market for creditpricing purposes. Again, the pricesthus obtained would fully incorporatethe bank’s internal risk assessments ofthe respective credits and relatedmarket prices of credit risk, but theywould rightly be independent of abank’s legacy credit portfolio and itsother activities.

Whatever the route to credit pricing,the important message is that to obtainrisk-adequate credit prices, a bank mustcarefully study and understand thecharacteristics and dynamics of therating systems it is using. These factorswill become fully apparent only byobserving the behavior of a given ratingsystem through a business cycle, andthey will be dependent on the ratingculture in a given organization. It is upto the credit risk controlling unit togather and analyze relevant data andlink the internal Basel II rating systemsto relevant external models and henceto market prices for credit risk.

The price that will then be quoted tothe client will, however, not be theresult of a pure model calculation butwill depend on many factors includingthe techniques available to the bank forrefinancing the loan and hedging theassociated credit risk. The more flexibil-ity in these techniques, the morecompetitive the bank is likely to be.

16 Fitch CDS Implied Ratings (CDSIR) Model, Fitch, 200717 Small and medium-sized enterprise collateralized loan obligation. As an example consider S&P’s “Credit

Risk Tracker“

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Those companies that havebeen able to maintain focuson the value creation aspectof the newly introduced riskmeasurement methodsthroughout the Basel IIimplementation process arelikely to find themselveswith competitive advantagein the post-Basel world.

Ben Begin (United Kingdom), FrankGlormann (Germany), and Steven Hall(United Kingdom), show how, by build-ing on and combining the newly intro-duced scoring methodologies withcustomer value analytics, the creditprocesses and the general interactionwith retail and small-businesscustomers can be improved and more value can be created.

A key requirement of Basel II is to inte-grate the credit scoring methods usedfor regulatory purposes into the creditrisk management processes. This inte-gration along with linking those meth-ods to customer-value considerations,can enhance the credit processes toallow faster growth and value creation.

The improvements relate to fourelements in the relationship with a customer:

• Simplifying and streamlining thecredit processes as a result ofimproved risk measurement techniques

• Improving credit decision makingbased on customer values

• Leveraging customer value informa-tion for sales optimization purposes

• Preserving value by improving theearly warning, collections, and recoveries processes

Growth ProcessesLeveraging Basel II Scoring Models and Data to ImproveCredit Processes

Simplifying and Streamlining Credit ProcessesThrough the advent of statistical scoring tools, credit decisions are often madeon the basis of automated scorings (Figure 11).

Yet, an average of less than 50 percent of all credit decisions in mortgage banksacross Europe that are made on the basis of an automated scoring is indicativeof considerable room for efficiency improvement in the credit buisness withretail and small business customers.

Managing Credit Risk: Beyond Basel II 20

Figure 11: Decision Ranking for Mortgage Applications Across European Markets

Source: European Mortgage Federation, 2006

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In fact, automated decision-makingapproaches using statistical scorecardsallow companies to consider opportuni-ties to centralize credit risk manage-ment and measurement as well as theoutsourcing of the operational manage-ment to specialist providers. Theseactions can give rise to improved qual-ity and consistency of credit decisionmaking as well as cost savings.

However, beyond these results, thekey area on which to focus is valuegeneration in customer interaction andopportunities for its improvement.Here we are looking to move from apure risk-based cutoff (in line withBasel use-test requirements) to avalue-based decision metric.

Improving Credit DecisionMaking Based on CustomerValuesRetail companies have wanted toimprove ”customer-value management”for a long time. Basel has created theopportunity by “forcing” them tocapture robust and granular dataacross all customers, thereby allowingconsistent analysis across segments.Using this and other data in a frame-work that combines risk, marketing,and finance models, banks can deter-mine the risk-adjusted profitability ofthe client relationship. The easiest wayto approach this problem is to considerthe current customer value only. Moreadvanced models also would takefuture customer value into account.

21 Managing Credit Risk: Beyond Basel II

Strategies for interacting with the customer may be defined more appropriatelyand flexibly depending on the result of the customer-value calculation. In particu-lar, a focus on risk-adjusted profitability of the customer relationship would revolu-tionize credit risk decision making, especially in the retail context, where a focuson customer value may lead to a different result than a purely risk-based decision(Figure 13).

Even though the concepts are not difficult to master, the implementation is likelyto be fraught with challenges. A typical issue is implementation of the valueconcept throughout the organization, most importantly in the distribution unitswhere incentives must be linked to the underlying value concept rather than puresales volume. The two factors can, however, be reconciled using the concept ofrisk appetite (Figure 14).

Figure 12: Customer Value Concepts

Source: KPMG in Germany, 2007

Figure 13: Risk- or Value-Based Cut-off

Source: KPMG in Germany, 2007

CreditRisk0

High Value, Low Risk

High Value,High Risk

Low Value,High Risk

Value

Value -BasedCut -off

Low Value,Low Risk

Credit -RiskCut -off

Both measures would restrict doing business in this region

Value destroying

Too Risky

Credit Risk_printer.qxp 4/10/07 2:06 pm Page 21

Following a four-step procedure is appropriate:

• First, define the bank’s risk appetite, which determines the risk-return trade-offit is willing to accept.

• Second, agree on a targeted sales volume.

• Third, set the rejection criteria to ensure rejection of credit applications likely todestroy value while ensuring the feasibility of meeting sales targets.

• Finally, monitor and adjust the combined “Credit Risk & Value Cut-off” on anongoing basis as the “Risk Appetite” changes and / or the underlying valueconcept becomes commonly accepted, also within the sales organization.

Leveraging Customer Value Information for SalesOptimization PurposesFurther leverage can be gained by combining customer risk and value informationas well as market understanding with propensity models, which help to identifylikely customer behavior. The results can be used to customize and target productofferings, providing greater flexibility and greater customer focus without compro-mising measurement or quality. In addition, the bank’s sales staff is given greaterinsight into customers’ risk and value profile in the initial customer interaction(Figure 15).

Managing Credit Risk: Beyond Basel II 22

Figure 14: Combined Cut-off from Value and Risk Dependent on Risk Appetite

Source: KPMG in Germany, 2007

Figure 15: Combining Customer Value Analytics with Propensity Information to

Increase Revenue Base

Source: KPMG in Germany, 2007

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Such improvements require control toensure that no arbitrage or gaming ofrating systems takes place amongsales functions. They also call forgreater systems capability to dealwith the improved customization ofsales offerings and performancemeasurement. Figure 16 illustrates afront-end tool—a ”Sales Cockpit”—providing distribution people with rele-vant information for client meetingsand sales activities—e.g., basic infor-mation on the customer, customer’ssegment information, customer’sbanking volume in terms of specifiedRAP/value metric, customer’s attritionrisk, customer’s current productusage, customers’ propensities/affini-ties for the (unused) products of abank’s product portfolio, and realizedand unrealized wallet-share, informa-tion-specific distribution activities.

23 Managing Credit Risk: Beyond Basel II

Preserving Value byImproving the EarlyWarning, Collections, andRecoveries Processes With a focus on the rising level ofarrears worldwide, companies mustconsider early identification of default-ers, looking at pre-delinquency asstandard.

The enhanced Basel II models andtools that organizations have developedshould permit improved early warningaccount management. Institutionsshould look to combine traditional“early warning” measures (for exam-ple, missed payments) with quantifiedrisk measures produced by the Baselmodel suite to optimize arrearsmanagement performance. Thisapproach should allow institutions tomeet the challenge of being appropri-ately risk-sensitive and dynamic.

Defaults and the need for collectionand recovery processes will continuedespite these efforts. Basel models, inparticular the loss given default predic-tive models, together with customervalue models are likely to become acore part of banks’ collection andrecovery strategies. These ideas will befurther explored in the section on “TheValue in Bad Debt” at the end of thisdocument.

Figure 16: Sales Cockpit Containing All Relevant Information for Sales Optimization

Source: KPMG in Germany, 2007

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Managing Credit Risk: Beyond Basel II 24

Figure 17:Tendency to Increase Numbers of Arrears (U.K. and Germany)

Source: KPMG International, 2007

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While a number of leadingbanks have been success-fully testing the waters forsome time, a more activeand portfolio orientedapproach to credit riskmanagement is starting toappeal to a larger audience.

Vijay Krishnaswamy and ChristianHeichele (both, Germany) provideinsight into the impetus for investing inactive credit portfolio management(ACPM) capabilities, implementationapproaches and phases, and potentialbenefits and challenges.

An Orchestrated Approach to Value CreationOpportunities and Challenges in Active Credit Portfolio Management

25 Managing Credit Risk: Beyond Basel II

While Basel II has preserved a strictfocus on individual loan exposures,markets are progressively taking aportfolio view on credit risk manage-ment. This shift has been fostered by anumber of mutually reinforcing devel-opments:

• Improved risk measurement tech-

niques. Most banks have investedsubstantially in risk modeling capabil-ities and data warehousing as part oftheir Basel II compliance efforts.These efforts have improved thequality of credit rating tools and riskestimates and, perhaps for the firsttime, given rise to serious efforts toaddress data quality issues andimprove the quality of reportingderived from those data.

• Continuing market growth for

credit derivatives and structured

credit products. The credit deriva-tives market has grown spectacularlyover the past decade—faster thanany other derivative market. The totalnotional value outstanding of creditderivatives has increased by morethan 100 percent over the past twoyears, with market size exceeding$20 trillion by the end of calendar-year 200618. This development hasbeen accompanied by a further stan-dardization of products and thegrowth in market share of indexproducts, which in turn hasincreased liquidity. New productssuch as loan CDSs are starting toemerge, providing both investorsand protection seekers with a widerarray of instruments. A natural

18 British Bankers Association, BBA Credit Derivatives Report 2006

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consequence is that bank seniormanagement is keen to explore howsuch instruments may be used tomeet their strategic objectives.

• Improved pricing and incentive

plans. By actively managing creditrisk and thereby making use of anincreasing range of capital marketinstruments, banks can price betterversus the market, increasing theircompetitive positioning and thelonger-term sustainability of the busi-ness. The other side of the coin isthe need to create for sales and rela-tionship managers incentives thatare more aligned with the bank’sobjectives and to increase the trans-parency of their value-added contri-butions.

• Focus on the management of

concentration risk. While the volatil-ity in earnings due to unpredictablecredit losses has always been aconcern, banks have sharpened theirfocus on bulk or concentration risk—that is, the possibility that a singlelarge credit exposure to whatappears to be a creditworthyborrower or sector could quickly turninto a huge loss. This possibility hasled banks to look for ways to better,and more nimbly, manage such risks.

• Focus on value-creating use of risk

capital. Credit businesses have tradi-tionally followed buy-and-holdmodels, relying on strong customerrelationships and the syndicationmarket to gather assets. However,with the availability of new, deepermarkets, managing exposure inflowsand outflows has been taken to anew, more active level. This develop-ment has been helped by the realiza-tion by banks that one of their keyvalue levers is to increase the veloc-ity of capital turnover. Typically, creditrisk capital is the largest componentof capital utilization. Thus, unlockingeven a small piece of value in thisarea directly translates into the overall value of the bank.

Managing Credit Risk: Beyond Basel II 26

ACPM Business Models Credit portfolio management units are implemented as central units that managecredit risk across the bank, typically for the following portfolios:

• Wholesale loan portfolio (e.g., SME, large corporation)• Investment portfolio (e.g., bond portfolio)• Trading book (e.g., counterparty risk)

Banks’ approaches to implementing credit portfolio management are quitediverse but can be generally classified along two fundamental models (see thetable below):

• Active portfolio trader, with a focus on relative value investing• Portfolio optimizer, with a focus on managing concentration risks

In addition, there are hybrid models combining features of the above.

Figure 18 is an illustration of the typical setup of an ACPM unit showing keyfunctional interaction and risk transfer.

Portfolio Optimizer Active Portfolio Trader

Mandate andPhilosophy

• Focus on portfolio optimiza-tion by reducing name-levelor industry-/geography-levelconcentrations

• Reduction in regulatory oreconomic capital to createcapacity for new business

• Full ownership of credit risk• Generation of positive risk-adjusted

returns through exploitation ofmarket opportunities

Key Activities • External risk transfer throughcredit derivatives or securiza-tion of parts of the loan book

• Advisory role in originationand pricing

• Relative-value investing to optimizeportfolio diversification and risk-adjusted return

• Direct impact on origination andpricing decisions

PerformanceMeasurement

• Cost-center approach orprofit-center approach usingaccounting-based P&Lmetrics

• Profit-center approach, with fullownership of credit risk reflected inRAROC and EVA metrics

• Often combined with internal transfer pricing to transfer credit risk from origina-tion business to CPM

Figure 18: Illustrative Example for a Bank’s Organizational and Functional Setup

with ACPM

Source: KPMG in Germany, 2007

Note: RAROC = risk-adjusted return on capital; EVA = economic value added; CPM = credit portfoliomanagement

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Phased EvolutionBanks that want to develop a modelfor ACPM usually embark on an evolu-tionary approach to the mandate,tasks, and targets for the ACPM unit.

Before reaching the “portfolio opti-mizer” or “active portfolio trader” level,banks typically start with a reactivedesign of their ACPM model. In thisfirst phase, banks move from the gath-ering, analysis, and reporting of portfo-lio risks that is often performed bycredit risk control to implementing aspecific unit that sets principles fordealing with portfolio risk such asconcentration risks. It might also setout guidelines to influence the portfolioinflow by means of setting hurdle ratesfor profitability or minimum ratings ofcounterparties. In a next step, theCPM unit might move from influencingportfolio inflows to a more activeapproach by directly influencing theportfolio outflows via portfolio hedginginstruments, such as securitizations, orindividual hedges, such as CDSs.However, it is only when CPM obtainsfull ownership of risk and responsibilityfor hedging and reinvestment deci-sions that a truly active approach toCPM is established.

While the evolutionary steps toward an active management of credit riskare similar in most banks, differenttarget levels—and speeds toward real-ization—are typically applied for differ-ent parts of the portfolio. Banks thataim for the active credit risk tradingdesign usually first limit this approachto the most liquid areas—for example,large corporate portfolios wheresecondary loan trading or CDS transac-tions are available. However, with thegrowth of secondary markets and newinnovative product designs, they willlikely extend their efforts to other port-folios in the future.

The ultimate choice of approach andphasing is a careful decision thatneeds to consider the nature of theportfolio, especially in terms of recycla-ble risk, size, capital position, businessstrategy, peer group practices, regula-tory environment, and infrastructure.

27 Managing Credit Risk: Beyond Basel II

Benefits from ACPMThe potential benefits of active creditportfolio management are significant.They can include:

• Lower economic capital require-ments due to better management ofconcentration risk

• Higher transparency regarding valuecreation in the credit business thatallows for improved incentivesystems/performance measure-ment/value allocation (distributionversus risk taking)

• Better pricing of origination business• Revenue generation through relative

value investing• Improved capital utilization

Implementation Challenges Governance and Organization

A key issue in the implementation of acredit portfolio management unit is theorganizational setup and the questionof reporting lines. Typical alternativesinclude integration into the chief riskofficer area, a front-office businesssuch as a trading and investment bank-ing unit, or the treasury function. Whilecriteria such as availability of skilledresources, current infrastructure, regu-latory aspects, and implications forbusiness processes and interfaces may play an important role in determin-ing the best organizational setup, onehas to acknowledge that strategic andpolitical aspects cannot—and shouldnot—be ignored. Given that the imple-mentation of ACPM is closely tied toperformance expectations in terms ofeconomic value added, capital effi-ciency, and reduction in bad debts, thisdecision is often one of the most criti-cal design aspects of such a function.

Cultural Change

An ACPM model is typically accompa-nied by a transfer of credit risk into acentralized team, which is a change inthe roles and responsibilities of thetraditional sales functions, which typi-cally “own” the credit risk—includingthe credit risk margins—of the loansthey originate. Further, the implemen-tation of a transfer pricing approachalso calls for a change in the originationmindset to drive greater focus on pric-ing, deal structuring, and cross-sellingpotential, apart from traditional relation-ship banking concerns.

Internal Performance Measurement

Implementing ACPM places theperformance of the bank’s credit busi-ness in sharper focus. Internal creditrisk transfer pricing mechanisms allowfor a separation of credit margins fromother performance elements andthereby increase transparency onwhere value is being created—andwhere it is being destroyed. This trans-parency has a profound impact onincentive systems and therefore mustbe well designed to avoid moralhazard. Additional layers of complexitymight be needed if different parts ofthe portfolio are managed under differ-ent premises—for example, tradingversus banking book exposures—which in turn underscores the need forwell-designed phasing of new keyperformance indicators.

Multidisciplinary Approach

Implementing a CPM model typicallyrepresents a significant interactionamong numerous bank functions—including, for example, sales, trading,risk management, treasury, manage-ment information systems, finance,tax, and compliance. This importantinteraction adds substantially to thecomplexity of the project and requiresthat leaders obtain a dedicatedmandate with clear implementationaccountability, together with strongboard and senior management support.

Scarcity of Resources and

Know-How

Demand is high for people with thediverse skill sets to manage credit port-folios, but the available pool of talent issmall. Capabilities include a goodunderstanding of, and experience in,primary and secondary credit markets;quantitative skills in single-productvaluation as well as portfolio modeling;and knowledge of regulatory andaccounting requirements.

IT Infrastructure and Data

Availability

From an IT perspective, ACPM can beeven more demanding than Basel II.While leveraging the Basel II IT archi-tecture to obtain risk and capital data isa natural approach, these data repre-sent but one key input into ACPM’sdecision-making processes. ACPM alsoneeds to consider risk from an

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Managing Credit Risk: Beyond Basel II 28

economic perspective—through meas-ures such as credit VaR or economiccapital as well as the return on aloan—to get a complete picture of therisk-return relationship. The quality ofthe data is another important issue. Ina positive sense, the use of such datain real day-to-day portfolio manage-ment decisions actually helps uncoverchallenges and provides opportunitiesto address them.

Methodological Challenges: Credit

Risk Capital, Pricing of Illiquid

Positions, and Atypical Structures

Ideally, CPM transfer prices are basedon observable market prices. This pric-ing is not possible for exposures thatlack such benchmarks, and often repre-sent the bulk of the portfolio. A keyrequirement to price such exposures isthe methodology to determine howmuch risk they contribute to the portfo-lio using measures such as credit riskcapital (hence capturing diversification)and concentration effects. Further, non-standard structures are uncharted interms of robust pricing methodologies.To integrate these exposures into anactive management of credit riskrequires a proxy or a mark-to-modelapproach, both of which have draw-backs. While the former approachsuffers from the lack of a closely repre-sentative proxy, the latter will typicallyleverage existing pricing plans basedon Basel II parameters. As shown inthe section “Pricing at All Costs,”suchBasel II based approaches might notalways produce results consistent withexternal market observations andtherefore would have to be adjustedcarefully.

External Performance Management:

P&L Volatility

The active use of credit derivatives tomitigate credit risk positions results ina greater possibility for volatility in theprofit and loss statement (P&L), arisingfrom frequent changes in the mark-to-market valuation of such instrumentsunder IFRS rules19. Although theseissues can be addressed in numerousways, each has its own complications.

At the extreme, hedge accounting forcredit risk could be a beneficial option;however, IAS 39 imposes stringentrequirements, the fulfilment of whichmust be demonstrated on a regularbasis over the life of the hedge. Morewidely applied alternative solutionsinclude the application of the fair valueoption or the use of financial guaran-tees under IAS 39.

Compliance

The ACPM function usually trades withthe market through the bank’s tradingdesk, at least in the active portfoliotrader model, and is therefore subjectto compliance with regulations such asthe EU’s Markets in FinancialInstruments Directive. On the otherhand, the sales and credit monitoringfunctions typically have access toprivate information that must not bemade available to the trading func-tions. Thus, the information flows toand from the ACPM function must beclearly defined and implemented.

In sum, banks need to be aware thatthe implementation of an active CPMunit involves many potential pitfalls andrepresents a significant investment.However, the benefits usually signifi-cantly outweigh the costs involved.

OutlookA number of banks have substantiallyprogressed in their Basel II implemen-tations and are now looking ahead atevolving challenges. One consequenceof the Basel II rules is that regulatorycapital requirements will become morevolatile because capital levels are nowexplicitly linked to probability of defaultand loss given default measurements,which tend to be cyclical. The implica-tion for banks is that they will haveless control over their capital adequacypositions versus their targeted ratios.In this context, the role of CPM willgain in importance because the mainsource of volatility is credit risk, whichis also typically the single largest risktype for most banks. As a result, mostbanks are expected to expand theirperspective on CPM.

Volatility of market credit spreadscombined with high leverage ratiosrequire high market awareness and anoptimized setup to avoid unwantedrisks as evidenced by the recent devel-opments in the credit markets in thewake of the subprime crisis.

Finally, while only a few big players arelikely to make full use of market oppor-tunities—given the need for a criticalmass to justify the substantial invest-ment an ACPM requires—many moremid-sized to large banks are likely todevelop their own, customizedapproach to a more active manage-ment of credit risk.

19 Under IAS 39, derivatives, including credit derivatives, generally need to be valued at fair value and thushave a direct impact on the P&L.

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Over the past decade,developments in credit risktransfer have included asignificant evolution intraded credit products, suchas credit derivatives andsecuritizations.

Elizabeth J. Murphy (Canada) highlightssome of the new and complex chal-lenges that could arise during a corpo-rate restructuring process in which thedebtor or subject of the restructuring isthe subject of a credit derivative cover.

The rapid growth in the credit risktransfer markets has contributed to thedistribution of risks across markets,both within and outside the traditionalfinancial sectors. In the credit deriva-tives market, some unease prevailsconcerning its mostly OTC-basednature, including possible hidden coun-terparty risks and disruptions in theinfrastructure that have not been fullytested by an extended or severe creditdownturn. Restructuring market partici-pants, such as lawyers, financial advis-ers, and scholars, have also voicedconcerns about the significant develop-ments in the risk transfer markets.Credit derivatives are an importantaspect of credit risk transfer markets;this section addresses the implicationscredit derivatives may have for corpo-rate restructurings.

Overview of the CreditDerivatives MarketsThe credit derivatives market began inthe early 1990s, driven primarily bylarge banks that wanted to managetheir credit risks more effectively andefficiently by transferring default risk tothird parties, thereby reducing creditconcentrations and diversifying theirexposures. The British Bankers’Association (BBA) estimates that theglobal market for credit derivatives willbe USD20 billion by the end of 2006,

Calculations with many UnknownsThe Implications of Credit Derivatives for CorporateRestructurings

29 Managing Credit Risk: Beyond Basel II

expanding to USD33 billion by the endof 2008, and notes that the market isnow a trading market (Figure 19).20

The exponential growth in the volumeof credit derivatives has been attrib-uted to a variety of factors, includingincreased liquidity in the market

(coupled with the increased appetitefor risk and the commoditization ofrisk), the increasing participation ofhedge funds in the credit derivativemarket, and improved standardizationof terms. An additional factor is theincreasing variety of traded credit

20 For additional information on size of the global credit derivative market, refer to BBA Credit DerivativesReport 2006 (British Bankers’ Association, London, 2006).

Figure 19: Global Credit Derivatives Market

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derivative products from credit defaultswaps to synthetic collateralized debtobligations (CDOs) to tradable indexes.

While the banks are still the dominantplayers in the market, their share hasdeclined as hedge funds and othermarket participants increasingly take agreater share of both the buy- and sell-

Managing Credit Risk: Beyond Basel II 30

side of the market (Figures 20 and 21).The BBA observes that while banksstill constitute the largest marketparticipant as both buyers and sellersof credit protection, two thirds of thederivatives volume of banks is nowdue to trading and a third is related totheir loan book. According to FitchRatings, the net sold and bought posi-

tions within the banking sector variesbetween regions and between largerand regional banks.

Among the U.S. banks, five “dealerbanks”—J.P. Morgan Chase, Citibank,Bank of America, Wachovia, and HSBCBank USA—carry out 77 percent of thecredit derivative volume. These institu-tions “house the vast majority of thenotional exposure and virtually all ofthe ‘protection sold’ volume. The majorsecurities firms and, to a lesser extent,a few major Canadian banks, representthe bulk of the remaining notionalvolume in North America….The otherU.S. and Canadian banks that usecredit derivatives employ them prima-rily as a risk mitigant against exposuresin their banking book.”21

While investment-grade corporate obli-gations (i.e., those rated BBB or better)have comprised most of the underlyingcredit transferred in the credit deriva-tives market to date, the percentage ofbelow-investment-grade obligationstraded in the market is expected toincrease. According to Fitch Ratings,the global credit derivative exposuresby rating sold for below-investment-grade was 31 percent in 2006, up from8 percent in 2002. Drivers of a movedown the credit curve include a furtherexpansion of the credit derivativemarket, tight spreads, and a decline inthe number of higher-grade issues.

New Challenges FacingRestructuring ParticipantsSo what are some of the potential newchallenges on the restructuring envi-ronment due to the existence of creditderivatives?

The answer to this question is notclear for several reasons. First, becausethe credit derivative market is not verytransparent and the existence of a CDSmay not be disclosed, determining if alink exists between the presence ofthe CDS and the dynamics of theparticular restructuring is not alwayspossible. Second, while credit marketobservers have reported problems withcredit derivatives during a restructur-

Buyers of Credit Protection – Market Share

Type 2000 2002 2004 2006 2008(estimate)

Banks – Trading activities81% 73% 67%

39% 36%

Banks – Loan portfolio 20% 18%

Hedge funds 3% 12% 16% 28% 28%

Pension funds 1% 1% 3% 2% 3%

Corporates 6% 4% 3% 2% 3%

Mono-line insurers

7%3%

2% 2% 2%

Reinsurers 3% 2% 2%

Other insurance companies 3% 2% 2% 2%

Mutual funds 1% 2% 3% 2% 3%

Other 1% 2% 1% 1% 2%

Source: BBA Report 2006

Sellers of Credit Protection – Estimate of Market Share

Type 2000 2002 2004 2006 2008(estimate)

Banks – Trading activities63% 55% 54%

35% 33%

Banks – Loan portfolio 9% 7%

Hedge funds 5% 5% 15% 32% 31%

Pension funds 3% 2% 4% 4% 5%

Corporates 3% 2% 2% 1% 2%

Mono-line insurers

23%21%

10% 8% 8%

Reinsurers 7% 4% 4%

Other insurance companies 12% 3% 5% 6%

Mutual funds 2% 3% 4% 3% 3%

Other 1% 0% 1% 1% 1%

Source: BBA Report 2006

Figure 20–21: Buyers and Sellers of Credit Protection Market Share

21 Fitch Ratings, “Global Credit Derivatives Survey: Indices Dominate Growth as Banks’ Risk PositionShifts” (Fitch Special Report Financial Institutions, Septembers 21, 2006) <derivativefitch.com>

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ing, no firm evidence exists on thefrequency of such problems.22 However,given a possible combination of acontinuing expansion of the creditderivative market, coupled with afurther weakening in the credit qualityof the corporate entities referenced byCDSs and an eventual deterioration inthe credit cycle, debtors will likely bethe reference entity in credit derivativestructures and the existence of thecredit derivatives may have a signifi-cant impact on workout situations.23

A number of new challenges facerestructuring participants:

• The debtor, the protection buyer, andthe protection seller may all havedifferent economic interests, andthese interests may be affected bydifferent workout strategies. Thesedifferent interests in turn may affectthe structure and terms of anyproposed restructuring plans.

• The existence of CDSs will create agreater challenge to building consen-sus in the early stages of distress inan attempt to restructure outside offormal insolvency proceedings or toenter formal proceedings with awell-developed reorganization plan.Reasons include a lack of certaintyas to who should be at the restruc-turing table and partial or completedivisions in economic and legal rights.

• In the early days of working out aproblem credit, a debtor may notknow if it is a named referenceentity in a CDS contract. Due to lackof transparency in this market, adebtor may have difficulty determin-ing who holds the CDS. This informa-tion will not be in company records.Additionally, the debtor will not easilyknow how many times the creditrisk may have been transferredthrough the credit market.

• Some parties with an interest in therestructuring may not want to partici-pate in restructuring negotiations inorder to avoid receiving insider infor-mation. For example, a counterpartyto a CDS may wish to preserve itsoption to trade its credit exposure.Alternatively, a counterparty mayseek to buy up other claims of thedebtor to create a strategic positionin the workout negotiations.

31 Managing Credit Risk: Beyond Basel II

• The party with the largest economicrisk may not be involved in therestructuring discussions.

• If there are multiple holders of creditrisk through different credit deriva-tive contracts, they will have differ-ent considerations—such as theamount and duration of the cover,the events that may trigger it, andthe means of settlement—withrespect to the scope and timing oftheir risk coverage. For example, thedebtor may find that the protectionbuyer refuses to agree to amend-ments to its credit documentation,such as a payment change or defer-ral and changes to covenants thatwould otherwise trigger a creditevent. Or, the protection buyer maybe unwilling to agree to the exten-sion of the maturity date beyond theprotection period unless a creditevent has already occurred or theextension itself qualifies as a creditevent. Holders of protection can alsobe expected to weigh the benefitsand risks of letting a credit eventoccur, or in extreme circumstances,precipitating one.

• After a credit event, credit positionsmay settle and the number of interested players may change orincrease. These new participants willneed time to get up to speed, whichcan lead to delays and perhaps revi-sions and new compromises.

New Challenges FacingCommercial Banks24

Commercial banks have been shiftingfrom traditional buy-and-hold exposurebusiness models to originate-and-distribute business models.Developments in credit portfoliomanagement, including the use ofcredit derivatives, are a significantreason for this shift. However, in addi-tion to the challenges facing restructur-ing participants described above, thecommercial banks, depending onwhether they are buyers or sellers of

credit protection, could face someunique challenges arising from beingparticipants in the credit derivativesmarket. For example:

• Traditionally, relationship lending has been an important aspect ofcommercial banking, and creditderivatives may enhance bank-clientrelationships with a positive impacton bank loan supply, which in turnshould lead to easier credit condi-tions at lower rates. However, if abank is a buyer of credit protectionthrough credit derivatives, and if thebank needs to restructure a loanwith a borrower and prefers to reachan amicable work-out, the seller ofprotection, such as a hedge fund,may not have the same incentive toachieve the bank’s goal.

• Commercial banks need to establishappropriate infrastructure and inter-nal controls to ensure that clientconfidentially and insider tradingrules are not breached by the trans-fer of information between the loanorigination area and the credit deriva-tive trading desk.

KPMG’s research indicates that newand complex challenges could ariseduring a corporate restructuringprocess where the debtor or subject ofthe restructuring is the subject of acredit derivative cover. However, theextent of any effect will depend on thespecific facts in each situation and thedetails of the underlying arrangements.

22 INSOL Lenders Group, Credit Derivatives in Restructurings, Guidance Booklet (INSOL International,2006, London). The lending group also reports that there is no evidence “that CDSs have caused anotherwise viable restructuring to fail.”

23 Murphy, E., Sarra, J., and Creber, M., “Credit Derivatives in Canadian Insolvency Proceedings: ‘TheDevil will be in the Details,’ ” Annual Review of Insolvency Law 2006 (Thomson Carswell, Toronto,2006)

24 European Central Bank, Financial Stability Review, December 2006, www.ecb.int/pub/pdf/other/finan-cialstabilityreview200612en.pdf; Cole, R.T., Felberg, G., Lynch, D., Board of Governors of the FederalReserve System, “Hedge funds, credit risk transfer and financial stability,” Financial Stability Review,Special Issue Hedge Funds (Banque de France, April 2007)

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A critical success factor forthe efficient managementof sub- and non-performingloans—also known as debtmanagement—is decisionsupported by accuratemeasurement instrumentsalong the entire debtmanagement value chain.

Wolfgang Malzkorn, Peter Rindfleisch,and Frank Glormann (all, Germany)explain how this goal can be achievedusing a decision tree based onadvanced credit risk and customervalue measurement techniques.

The increasing number of sub- andnon-performing consumer loans insome major economies (e.g., Spain,

The Value in Bad DebtOn the Management of Sub- and Non-Performing Loans inRetail Banking

the United Kingdom, and the UnitedStates) has given rise to an increasedfocus on debt management in interna-tional retail banking. Advanced playershave already implemented, or are inthe process of implementing, “collec-tion scorings” to classify loans orcustomers according to the expectedsuccess of particular collection activi-ties as well as to determine the mostappropriate collection approach.

While these efforts are steps in theright direction, they need to beextended to cover the entire debtmanagement value chain from theoccurrence of the first repayment prob-lems to the stage when the loan orcustomer is either returned to theperforming portfolio or leaves thebank’s loan book because the contractis terminated, liquidated, or sold. Thiscomprehensive coverage can be

achieved by integrating the numerousdecisions required during the debtmanagement process into one coher-ent decision tree with correspondingrisk, profitability, and economic valuemeasures accompanied by detaileddecision rules. In addition to the usualcredit risk metrics, customer valuemodels play a crucial role in thiscontext. Moreover, the decision rulesneed to be defined in accordance withthe bank’s business model and collec-tions and recovery strategy.

Key Elements of DebtManagementState-of the-art debt management ismore than just a reactive function thatdeals with “erroneous” credit deci-sions. Instead, it is an active portfoliomanagement function for the sub- ornon-performing part of the loan book,which cooperates closely with the

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33 Managing Credit Risk: Beyond Basel II

underwriting, monitoring, general credit portfolio management, and analyticsfunctions. Its general objective should be to preserve as much economic value forthe bank as possible under the given circumstances. In doing so, it should adhereto the same principles and concepts that state-of-the-art banks apply to theirsales function, including the concepts of present and future value of both individ-ual deals and entire customer relationships.

To fulfill these objectives successfully, debt management requires a highlysophisticated and comprehensive approach (Figure 22).

Each case calls for a clear decision model and measurement instruments thatsupport it. An efficient process that is compliant with legal standards andcombines in-house activities and outsourcing in an optimal way further underpinsthe new vision. This process must be embedded in an organizational structurethat facilitates efficiency through functional specialization. Debt managementspecialists need to be sufficiently trained and provided with incentives in accor-dance with the principle of economic value creation as well as with the specificdebt management strategy. State-of-the-art information technology is needed tosupport activities, including data gathering, communications, decision engines,collateral management systems, and platforms for distressed debt sale. Finally, adebt management strategy should be formulated and implemented, creating thebasis for all decisions and customer communication.

Banks face a number of challenges in dealing with sub- or non-performing loans.At the same time, (new) instruments, methods, and techniques are available forpreventing (e.g., application and behavioral scorings, payment protection insur-ance [PPI]), treating (e.g., collection scorings, specialized collection agencies,advanced communication techniques), and mitigating the impact (e.g., risk-adjusted pricing, distressed debt sale) of bad debt. However, the use of thoseinstruments and methods should align with the bank’s business model and over-all strategy. Indeed, problems inevitably arise if banks use those instrumentsunsystematically or inconsistently with their business model or corporate strat-egy—possibly resulting in destruction of value, reputation damage, or loss ofcustomers. Examples of such effects created by questionable practices regardingPPI and portfolio deals have recently made headlines in the media.

Figure 22: Approach to Debt Management

Source: KPMG in Germany, 2007

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Measurement and Decision MakingBanks can avoid these problems and derive new benefits using a decision tree,based on the institution’s strategy, that helps prioritize key decisions related totreatment of sub-performing loans (Figure 23).

Four major decisions within the decision tree will likely be required:

• Should the bank launch procedures to cure the loan?• If not, should the bank restructure the loan, taking into account partial

write-offs and interest rate adjustments?• If not, should the bank terminate the entire customer relationship (or just

liquidate the sub-performing loan)?• If so, should the bank enter liquidation and recovery procedures or sell

the loan?

At each stage along the decision tree, appropriate measures and detailed decision rules can be specified that help to evaluate the alternatives.

Managing Credit Risk: Beyond Basel II 34

As illustrated in Figure 24, the objec-tive at #1 is to decide whether theloan can be returned permanently tothe performing portfolio, preferablywithout encountering any economiclosses, and which treatment ought tobe applied to achieve this aim. Keysuccess factors are the time horizon inwhich action steps are taken (sincethe cure rate typically decreaseswithin a very short period) and effi-

Figure 23:Time Structure of Decisions in Debt Management

Source: KPMG in Germany, 2007

Figure 24: Measurement and Decision Making

Source: KPMG in Germany, 2007

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cient communication with thecustomer to identify the reasons for re-payment problems and arrive at amutual agreement, if possible. Tosupport the decision that is to be madeat this point, it is important to measurethe probability of default or re-defaultof the customer (if cured).

Then #2 comes into play if the decisionin #1 has been negative—that is, thatthe loan cannot be returned to theportfolio. The objective will then be todetermine whether the bank shouldreplace the non-performing loan with aloan that might be less profitable thanthe original but still a worthwhileinvestment. Key success factors at thisstage are a timely response and deci-sion by the bank as well as a correctassessment of the customer’s proba-bility to (re-)default on the new loan.Relevant metrics at this point are:

• Future debt service capacity of theclient, considering the payment char-acteristics of the new loan

• Probability of (re-)default on the newloan

• Profitability of the restructured loan• Expected recovery from a liquidation

of the original loan (if it is not restructured)

35 Managing Credit Risk: Beyond Basel II

If the bank’s decision is not to restruc-ture, multi-product customers willenter #3 (Figure 25), whereas (in mostcases) single-product customers willbe directly routed to #4.

The goal of #3 is to determine theprospective relationship with thecustomer, based on default probabilitiesof the customer’s other products, ageneral reassessment of thecustomer’s debt service capacity, andthe customer’s value to the bank (infor-mation that would also be needed if thebank ultimately decided to initiatecollection and recovery procedures).Key success factors at this point are theefficiency of the collection and recoveryprocesses (in terms of costs and recov-ery amount), the appropriate collectionstrategy, the client communication toreach an agreement, and a correctassessment of the customer’s value tothe bank.

Relevant metrics at this point are

• Future debt service capacity of thecustomer’s other products with thebank

• Probabilities of default for thoseproducts

• The customer’s present and futurevalue to the bank

Figure 25: Final Decisions and Profitability

Source: KPMG in Germany, 2007

The detailed rules that determine thedecision at this point will focus onthose metrics. Thus, in addition to theusual risk measures, customer valuemeasurement methods and principlesenter the picture. It is, therefore,necessary that debt managementassume a customer perspective, ratherthan the usual product-related view.

Finally, the goal of #4 is to determinewhether selling the loan is more prof-itable than liquidation and subsequentrecovery. This question will be posedonly if the respective bank is in a posi-tion to perform non-performing loan(NPL) transactions in the creditmarkets and if a suitable deal isplanned. At the same time, however,banks of all sizes are increasingly gain-ing access to the skills necessary toenter into NPL transactions, mainly bysharing the use of relevant platformswith other institutions. Thus, the objec-tive of #4 is becoming relevant for anincreasing number of institutions.

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Key success factors at this point are anefficient recovery process (in thosecases where a decision has been madein favor of liquidation and recovery) andan efficient link and communicationbetween the debt management func-tion and the (credit) portfolio manage-ment function. In terms of supportingmetrics, the bank should be able toassess the expected recovery amountand costs of liquidation and collectionprocedures as well as the receiptsfrom adding individual loans to NPLtransactions.

Prerequisites and BenefitsAlthough such decision trees have acommon underlying general structure,they need to be customized to therespective banks’ business models andback-office functions. Thus, the effi-ciency of the debt managementprocess can be optimized and basedquantitatively on value-based manage-ment principles. The process needs thesupport of appropriate organizationalstructures in debt management andconsistent training and staff incentivesas well as state-of-the-art technicalsupport in customer communicationand workflow management.

Fortunately, evidence indicates that aredesign of the debt managementfunction is a worth-while investment.Recent studies and project experienceshow that given the average currentstandard in banks globally an invest-ment in the debt management functionas previously described will yieldsurprisingly high returns.

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kpmg.com

Contacts

Jörg HashagenManaging Partner AdvisoryGlobal Head Advisory Financial ServicesKPMG in Germany+49 69 9587 [email protected]

Daniel SommerPartner AdvisoryFinancial Risk Management Head ofRisk Methodology KPMG in Germany+49 69 9587 [email protected]

© 2007 KPMG International. KPMGInternational is a Swiss cooperative. Memberfirms of the KPMG network of independentfirms are affiliated with KPMG International.KPMG International provides no client serv-ices. No member firm has any authority toobligate or bind KPMG International or anyother member firm vis-à-vis third parties, nordoes KPMG International have any suchauthority to obligate or bind any memberfirm. All rights reserved. Printed in theUnited Kingdom.

October 2007

KPMG Contributors to this publication include Ben Begin, Pia Evertsson, Frank Glormann, Bernd Granitza, Steven Hall, Christian Heichele, Ralf Hennig, Vijay Krishnaswamy, Peter Lam,Marco Lenhardt, Wolfgang Malzkorn, Francesco Merlin, Elizabeth J. Murphy, Maxine Nelson,Peter Rindfleisch, Jürgen Ringschmidt, Tim Schabert, Daniel Sommer, Kristian Tomasini, Diane Nardin, and Carole Law.

GSC document code: GSC051

The information contained herein is of a general nature and is not intended to address the circum-stances of any particular individual or entity. Although we endeavor to provide accurate and timelyinformation, there can be no guarantee that such information is accurate as of the date it is receivedor that it will continue to be accurate in the future. No one should act on such information withoutappropriate professional advice after a thorough examination of the particular situation.

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