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Credit Risk Ratingat Australian Banks

Working Paper 7December 2000

Andrew McDonaldGuy Eastwood

CREDIT RISK RATING ATAUSTRALIAN BANKS

Andrew McDonald and Guy Eastwood

Working Paper 7

ABSTRACT

This working paper summarises a survey of the internal credit risk rating practices at 10

Australian banks. The survey is a response to international proposals to develop more risk

sensitive capital adequacy requirements utilising banks’ internal credit risk ratings. It

examines various elements of the Australian banks’ internal credit risk rating systems,

including the systems’ basic architecture, operating design and applications. Comparisons of

the survey findings with those from similar international surveys are also provided.

JEL Classification Numbers: G21

Keywords: Credit Risk Rating,APRA,Australia, Banks

Table of Contents

1 INTRODUCTION 3

2 OVERVIEW OF INTERNAL CREDIT RISK RATING SYSTEMS 5

2.1 SYSTEM ARCHITECTURE 5

2.2 OPERATING DESIGN 7

2.3 APPLICATIONS 8

3 CREDIT RISK RATING AT AUSTRALIAN BANKS 9

3.1 ARCHITECTURE OF AUSTRALIAN BANKS’ CREDIT RATING SYSTEMS 9

3.1.1 LOSS CONCEPT 9

3.1.2 NUMBER OF RISK GRADES 11

3.1.3 DISTRIBUTION OF EXPOSURES AMONG RISK GRADES 14

3.1.4 POINT-IN-TIME VS THROUGH-THE-CYCLE GRADING 15

3.2 OPERATING DESIGN FEATURES 16

3.2.1 EXPOSURES RATED 16

3.2.2 CUSTOMER PD RATINGS 16

(i) TYPES OF RATING PROCESSES 16

(ii) KEY RATING RISK FACTORS 18

(iii) LINK TO QUANTITATIVE DEFAULT STATISTICS 18

3.2.3 FACILITY LGD RATINGS 20

3.2.4 SYSTEM OVERSIGHT AND CONTROL PROCESSES 21

3.2.5 VALIDATION PROCEDURES 22

3.3 RATING APPLICATIONS 23

4 CONCLUDING REMARKS 25

APPENDIX 1:TYPES OF CREDIT RISK RATING MODELS 27

APPENDIX 2: EXAMPLE TWO-DIMENSIONAL CREDIT RISK RATING MATRIX 29

REFERENCES 30

APRA DECEMBER 20002

1 INTRODUCTION

Credit risk rating has become an important feature of most Australian banks’ credit risk management

systems over the past decade. This reflects the efforts of institutions to strengthen credit management

practices following the asset quality problems of the late 1980s/early 1990s, wider availability and growing

familiarity with rating techniques of increasing sophistication within the industry, and a growing array of

uses to which ratings may be applied. The Australian Prudential Regulation Authority (APRA) – as well

as its predecessor as banking supervisor, the Reserve Bank of Australia – has also sought to encourage the

use of credit rating systems, including through prudential guidelines originally issued to banks in 1995

and recently updated to apply to authorised deposit-taking institutions more generally.1

This paper surveys the internal credit risk rating systems currently utilised by Australian banks and

compares local rating practices with those found internationally. The main motivation for the paper stems

from proposals that have been put forward by the Basel Committee on Banking Supervision for the

reform of international bank capital adequacy guidelines, aimed at increasing the sensitivity of regulatory

capital requirements to differences in institutions’ individual risk profiles. The Committee has

recommended that, in conjunction with a new standardised approach to capital adequacy regulation, an

internal ratings based approach could also form the basis for determining regulatory capital charges. The

approach would link banks’ credit risk capital requirements to internal credit risk ratings for those

institutions with suitably robust and well-developed risk rating systems that are able to meet minimum

supervisory standards.2

The Committee’s recommendation means that prudential regulators, such as APRA, have need to

conduct further research into banks’ internal risk rating systems preparatory to commenting on, and

implementing, the next round of more detailed reform proposals. The stocktake of current industry

practice summarised in this paper constitutes part of that process.

The following section of the paper provides a general overview of banks’ internal credit risk rating

systems. It sets out some basic concepts and a broad structure for the main part of the report, which

summarises the main survey findings. This middle section focuses on the basic structure, operating design

and applications of the local banks’ rating systems and provides some international comparisons. The final

section offers some concluding remarks.

CREDIT RISK RATING AT AUSTRALIAN BANKS

APRA DECEMBER 20003

1 Refer Prudential Standards APS 220 Credit Quality available on the APRA website (www.apra.gov.au).

2 Capital adequacy regulation of banks and other authorised deposit-taking institutions in Australia, as in most countries, is based on international guidelines issued by the Basel Committee on Banking Supervision. The Basel Committee, which comprises central banks and bank supervisory agencies from G-10 countries, operates under the auspices of the Bank for International Settlements (BIS) and consults widely with supervisory agencies in other countries and with industry on prudential matters. The committee’s capital adequacy reform proposals are contained in a consultative paper, A New Capital Adequacy Framework, issued in June 1999.


APRA DECEMBER 20004

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2 OVERVIEW OF INTERNAL CREDIT RISK RATING SYSTEMS

Internal credit risk ratings are summary indicators of the degree of risk inherent in institutions’ individual

credit exposures. In combination, credit ratings also provide a useful snapshot of the overall quality of an

institution’s credit portfolio. A credit rating represents an assessment of the risk of loss from the failure of a

given counterparty to meet debt servicing and other payment obligations on a timely basis. Other, more

traditional, credit assessments tend to be binary in nature (covering decisions such as whether or not to accept

a new credit proposal or continue lending to a particular borrower). Credit risk ratings seek to be more

informative by grouping credit exposures with similar risk characteristics into a larger number of risk grades

or buckets.

There is no standard approach to risk rating credit exposures. Credit risk rating systems vary widely

among institutions, including in their basic structure, operating design and uses; the main elements of

which are illustrated in Figure 1.

2.1 System architecture

The basic structure (or architecture) of a credit risk rating system includes such features as:

• the risk concepts underlying the ratings;

• whether the ratings represent relative (ordinal) or absolute (cardinal) measures of risk; and

• the degree of risk differentiation sought (eg number of risk grades).

Abstracting from portfolio composition effects, the stand-alone credit risk associated with an individual

loan or other credit exposure depends on: (i) the amount at risk; (ii) the term of the exposure; (iii) the

likelihood of the customer defaulting during that period; and (iv) the likely severity of loss if default occurs.

Although an exposure’s term and probability of default are inextricably linked, these risk factors are

handled separately in most credit risk rating systems. Most systems assess the likelihood of customer

default over some common (often implicit) time horizon for all exposures irrespective of actual tenors.3

Consequently, credit risk ratings typically focus on the last two of the above-mentioned risk factors,

which when combined provide a measure of overall expected loss over the relevant time horizon.4

Expected loss in this instance has a statistical meaning, ie ratings do not represent predictions of actual

credit losses for particular loans, instead ratings are indicators of the credit losses likely to arise on average

over similar time periods from large numbers of loans that display similar characteristics.


APRA DECEMBER 20005

3 Differences in the maturities of exposures are usually dealt with at a later stage, eg when ratings are linked to numerical loss statistics for use in quantitative analyses, such as risk-based pricing,provisioning and capital modelling.

4 Expected loss = Probability of default x Loss given default

Table 1 shows that credit risk rating systems can be broadly classified according to whether the ratings

generated are ‘one-’ or ‘two-dimensional’ in form and which of the three loss concepts mentioned above

underlie the ratings, namely probability of default (PD), loss given default (LGD) or expected loss (EL).

One-dimensional ratings usually focus either solely on the probability of customer default (ie ignore the

impact of loss given default) or intermingle an analysis of default risk and loss given default into a single

overall assessment of expected loss. Two-dimensional systems, on the other hand, assign separate ratings

to each credit exposure relating to each of these risk factors.

Table 1: Types of credit risk ratings

Worldwide, a relatively small but increasing number of banks determine for each of their rated exposures:

• an obligor (or customer) risk rating – that reflects the probability of customer default based on

an assessment of quantitative and/or qualitative information relating to the customer’s

prospective financial stability;5 and

• a facility rating – that reflects loss given default. This rating is usually based on facility-level

characteristics such as product type, security coverage and/or seniority ranking.

Some institutions combine these two ratings into an overall composite risk rating, although most

subsequent internal analysis and reporting tends to be carried out at the sub-component level. At other

institutions the composite indicator is achieved simply by bringing the two ratings together in the form

of an alphanumeric label.

While usually classified as a two-dimensional system, a sort of hybrid approach is used at some institutions

around the world. A separate customer-level default risk rating is first determined, which is then adjusted

up or down according to the nature of each of the customer’s credit facilities. Typically, these facility-

based adjustments are restricted to one, or perhaps two, risk grades.


APRA DECEMBER 20006

5 Banks’ policy rules generally specify that all facilities to the same customer be assigned the same PD rating.While there may be instances where a customer defaults on one transaction and not another, as a practical matter, estimating a single PD for each customer is usually sufficient and avoids a more likely problem of having multiple ratings for a customer with little justification for the differences.

One-dimensional ratings

- Single obligor-level rating

- Single combined rating

Two-dimensional ratings

- Hybrid rating

- Composite rating

Loss conceptPD

(obligor-level rating)

LGD

(facility-level rating)

EL

(facility-level rating)

✔

✔

✔ ✔

✔

✔

✔

2.2 Operating design

A system’s operating design denotes the processes by which risk is evaluated and ratings assigned. It also

includes the surrounding controls and other measures designed to promote rating accuracy, integrity and

consistency throughout the rating organisation.

A wide range of risk evaluation processes exists. An important distinguishing feature of these processes

relates to the degree of subjectivity that is involved both in the selection of inputs and the way in which

those inputs are combined to derive ratings. At one extreme, ratings may be based on high-level

definitions judged by experienced lenders using traditional credit analysis – this approach is also followed

by many of the major external ratings agencies, including Moody’s and Standard & Poor’s. At other

institutions, such broad, subjective criteria are supplemented by more explicit guidance. This can involve

tools designed to assist raters in assigning ratings, including the development of quantitative and/or

qualitative rating benchmarks and/or formal debt rating models of varying levels of sophistication.6

However, even where more specific criteria have been introduced, the degree of subjectivity can vary

greatly, eg:

• individual raters may be able to exercise a good deal of discretion at institutions where simple

benchmarks and/or qualitative (rather than quantitative) model inputs are relied on;

• debt rating models differ widely in the extent to which they have been developed from

objective statistical analysis or expert judgement;

• institutions often use a mix of different techniques or models from among which raters must

choose; while

• at most institutions, raters are permitted to use professional judgement to override

model-based assessments.

The degree of subjectivity involved in the rating process can have important implications for the accuracy,

integrity and consistency of ratings within an organisation (Figure 2). Other things being equal,

appropriate controls and other measures designed to promote these desirable rating features will need to

be more extensive (costly) the greater the degree of subjectivity involved in the ratings process and the

greater the potential for rating outcomes to affect the performance assessments (and directly or indirectly

the remuneration) of raters. On the other hand, focusing on purely objective data, and/or reducing raters’

discretion to exercise judgement through model overrides, will ignore potentially relevant information

not taken into account by the rating model. It can also reduce incentives for lending and credit officers

to undertake adequate due diligence when assessing loan applications.


APRA DECEMBER 20007

6 Appendix 1 provides an outline of the main types of debt rating models used by banks.

Figure 2: Impact of subjective rating criteria1

How institutions attempt to work out an appropriate balance between these (and other) competing

influences can have a major impact on the overall design of their rating systems. This includes the choice

of methodologies used to assign ratings and other quality control measures, such as the design of policies

and organisational structures relating to the independent review of ratings, frequency of rating updates,

and responsibilities for the overall oversight and maintenance of the system.

Some other key aspects of a system’s operating design include:

• what credit exposures are covered by the system and what, if any, exposures are excluded;

• where different rating models/techniques are used, especially in different market segments, how

are ratings brought together into a single rating scale; and

• what processes exist to validate the (on-going) effectiveness of the ratings in differentiating risk.

2.3 Applications

As institutions’ familiarity with credit risk rating has increased, so too has the range and sophistication of

the uses to which credit ratings have been applied.

Credit ratings can be used to improve many aspects of credit risk management at the transaction,

customer and portfolio levels within a lending institution. In a recent survey of rating practices at large


APRA DECEMBER 20008

Moody’s CustomerRating

A1

A2

A3

Baa1

Baa2

Baa3

Ba1

Ba2

Ba3

B1

B2

B3

1 2 3 4 5 6

1 The diagram above shows the range of outcomes from a trial in which one bank asked several relationship and credit review managers to rate the same set of customers using identical information about those customers. Ratings were based on subjective high-level ratings definitions; the bank, at that time, had not introduced debt rating models or other rating tools to assist in the assignment of ratings.

US banks,Treacy & Carey (1998) grouped these uses broadly into analytic and reporting, as well as credit

administration applications. Analytic and reporting uses can include: monitoring and reporting of risk

positions; communication of differentiated credit risk acquisition and portfolio management strategies;

policy limits (eg large exposure limits); as inputs in loss provisioning, economic capital allocation and

risk-adjusted performance measures; and in risk-based pricing and employee compensation

arrangements. Administrative uses include such things as setting delegated credit approval thresholds and

trigger points for more intensive and/or specialist management of impaired and other problematic (eg

watchlist) loans.

Generally speaking, different uses will have different implications for the overall structure of credit rating

systems and the internal controls needed to maintain system integrity. For example, a system that is primarily

intended for tracking broad trends in portfolio credit quality generally would not need to be as sophisticated

as one intended for use in more complex capital allocation and risk-adjusted performance measures.

3 CREDIT RISK RATING AT AUSTRALIAN BANKS

The following sections of this paper describe the internal credit risk rating practices of 10 Australian

banks. Foreign banking groups operating in Australia were excluded from the survey, which also focused

on the banks’ rating of corporate and commercial exposures. Although credit scoring of banks’ consumer

portfolios has become widespread, only one bank in Australia has sought to implement a single rating

scale across the whole of its credit portfolio. A couple of the other banks rate consumer exposures on a

portfolio basis for APRA reporting purposes, however, this does not reflect broad internal reporting

practice at those banks as it tends to obscure higher-level monitoring of credit quality trends. This

situation is consistent with overseas experience; it reflects the fact that banks typically manage their

corporate/commercial and consumer portfolios differently using distinctly separate processes.

3.1 Architecture of Australian Banks’ Credit Rating Systems

3.1.1 Loss concept

As earlier noted, an important aspect of any credit risk rating system is the loss concept used to

differentiate the riskiness of different credit exposures, ie whether the ratings are one- or two-dimensional

in form and whether they focus primarily on PD, LGD, EL or all three credit risk measures.

All 10 of the surveyed Australian banks utilise two-dimensional rating systems. In rating their credit

exposures, each of these banks determines a separate customer-level PD rating, a facility-level LGD rating

and a composite EL rating.


APRA DECEMBER 20009

Other surveys indicate that only a small (though growing) proportion of overseas banks have similarly

structured ratings. Last year, for example, the Basel Committee on Banking Supervision surveyed nearly

30 banks across the G-10 that were identified by their national supervisors as having well developed

internal rating systems.7 In terms of the categories of rating systems identified in Table 1:

• about a third of the surveyed banks utilise two-dimensional ratings (of those, most use hybrid

ratings while only “a small number” assign separate PD and LGD ratings);

• 20 per cent use single facility-level ratings that explicitly take into account both obligor and

transaction specific characteristics; while

• the remainder (about half) assign single obligor-level ratings meant primarily to reflect the risk

of the borrower defaulting.

Two US studies,Treacy & Carey (1998) and English & Nelson (1998), made similar findings.Treacy &

Carey surveyed the top 50 US banks while English & Nelson surveyed over 100 US banks across different

size categories. Not unexpectedly, the latter found a higher proportion of smaller banks using one-

dimensional systems. Among those institutions using a two-dimensional approach, neither study cited

any examples of banks using composite ratings, though Treacy & Carey noted that “a few banks” planned

to shift in that direction.

The differences in the survey results partly reflect differences in the timing of the surveys (combined with

the rapid pace of development in this area) and the smaller number of banks in Australia. Nonetheless,

Australian banks appear overall to have moved more quickly in adopting two-dimensional composite

approaches to credit risk rating compared with their overseas counterparts.

Generally speaking, as internal credit risk ratings have been applied to a wider range of more sophisticated

uses, so too has the need for more accurate, more differentiated and more quantitative measures of risk.

By separately assessing default risk and loss given default, two-dimensional systems (and composite rating

systems in particular) can:

• improve communication about risk (including by conveying more detailed information about

risk profiles and by reducing potential ambiguity over the meaning of risk grades);

• lessen the tendency at some banks to rate primarily on the strength of available security;

• facilitate the development of rating tools to assist in the risk rating process;

• facilitate tracking of the accuracy of risk ratings;


APRA DECEMBER 200010

7 Basel Committee on Banking Supervision (2000a).

• are conceptually better aligned with the more sophisticated provisioning, capital allocation and

risk-based pricing techniques being developed at some institutions; and

• facilitate the alignment of internal and external credit ratings. As few banks have long enough

data sets to assign quantitative values to their internal risk grades based on their own loss

histories; many have sought to align their ratings with the experience of longer-established

external rating schemes (such as Moody’s and/or Standard & Poor’s). Alignment of internal and

external ratings is also likely to facilitate the development of secondary and derivative trading in

individual and pooled loans of customers that normally would not be rated by external

rating agencies.

In recent years, the above considerations have contributed to a greater or lesser extent to all of the

Australian banks either introducing composite systems where no rating system previously existed or

converting from simpler, more subjective, one-dimensional systems. In the case of the larger banks, the

move to composite ratings has been closely linked to the development of portfolio credit risk modelling

capabilities and more statistically based provisioning and capital allocation processes. Among the smaller

banks, however, it has had more to do with the banks’ wanting to improve their transaction-based credit

management processes as they have sought to enter into, or expand, their commercial lending operations,

including in new geographic areas. As part of this process, some banks have introduced externally

developed rating tools. As these latter products tend to focus on probability of default rather than overall

expected loss, an LGD dimension has been added.

The Australian banks combine their PD and LGD ratings to form an overall indicator of risk in a number of ways:

• the majority of banks simply bring the two ratings together in the form of an alphanumeric

label. For example, a rating of 1G might represent a low-risk borrower/high loss-given-default

(eg unsecured) facility while a rating of 7A might represent a relatively high-risk borrower/low

loss-given-default (eg well secured) facility;

• a couple of banks have developed look-up matrices. These matrices assign the same composite

rating to different PD and LGD rating combinations considered by each bank to imply similar

levels of expected loss. A hypothetical example of such a matrix is shown in Appendix 2.

3.1.2 Number of risk grades

Rating systems typically comprise one or more ‘pass’ grades plus several lower-quality ‘non-pass’ grades

reserved for exposures displaying varying degrees of credit impairment. Pass grades usually include those

rating categories that a bank considers to be of acceptable quality for new loans under its usual

underwriting standards and may also include one or two watchlist (or special mention) grades.

Internationally, the number of customer risk grades varies considerably among banks (Graphs 1 & 2).



Graph 1: Number of pass grades at international banks – Basel survey1

Graph 2: Number of non-pass grades at international banks – Basel survey1



0

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1 The distinction between pass and non-pass grades, though commonly used, is not standard nomenclature around the world. Consequently,banks responded inconsistently to the Basel survey when asked to split their risk grades into these two categories. Some banks, for example,appear to have included watchlist grades within the pass category while others have included these grades in the non-pass category; one bank reported that it had no non-pass grades.

0

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A general trend has been for banks to increase the number of pass grades as the range and sophistication

of uses to which their ratings are applied has grown. As long as raters can achieve the finer distinctions

required, rating systems with more risk grades – greater granularity – convey more information than

systems with fewer grades and can enhance a bank’s ability to analyse and model its portfolio of

credit risks.8

Larger banks also tend to have more customer risk grades than smaller banks. The cost/benefit analysis

of maintaining a larger number of risk grades tends to be more favourable for larger institutions. The

latter generally have more complex credit portfolios (comprising many more customers and a wider

spectrum of risk) and are more likely to have introduced other sophisticated techniques of portfolio

analysis that require ratings as inputs. Also, larger banks are usually better positioned, and have more

resources, to develop and support more granular rating systems (though with industry pooling of default

data becoming more prevalent, and the increasing availability of externally developed rating tools, this

may be becoming less of an issue).

With regard to the number of customer risk grades, Australian banks are mostly clustered around the

middle of the international spectrum (Graph 3). Most of the local banks have either 9 or 10 customer

PD grades; one large bank has 22 main grades. Another large bank, which has a general 9-grade

customer rating scale, utilises an expanded 19-grade scale for its large corporate customers. All of the

banks have from 3 to 5 non-pass/watchlist grades, though in some cases these administration grades may

be further sub-divided into a larger number of sub-grades.

Graph 3: Total number of customer risk grades at Australian banks



0

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1 10 1 2

8 Banks, however, need to exercise caution so as to avoid going beyond the point where they can no longer makemeaningful distinctions concerning the riskiness of different exposures. Banks that have linked portfolio risk modelling with risk-based pricing and/or profitability measures can face strong pressures in this regard, including from business lines looking for rating scale refinements to assist in meeting pricing and other performance targets.

Tighter clustering in the number of risk grades among Australian banks reflects the fact that several of

the smaller and mid-sized banks have recently (within the past two years) expanded the number of risk

grades as part of wider upgrades of their rating systems. Further change is occurring, with some of the

largest banks in the process of shifting to more granular rating scales. Like a growing number of their

overseas counterparts, the number of risk grades at quite a few Australian banks has been chosen to

facilitate comparisons with the rating scales of the major international rating agencies, Moody’s and

Standard & Poor’s.9

There is less clustering in the number of LGD grades, which are often based on security coverage ratios,

among banks in Australia (Graph 4).

Graph 4: Number of LGD grades at Australian banks

3.1.3 Distribution of exposures among risk grades

Regardless of the overall number of risk grades, the granularity, and therefore usefulness, of a bank’s rating

system will be reduced if credit exposures tend to be concentrated in only one or two risk grades.

Again, Australian banks seem to be broadly in line with current international practice (Graph 5). In

the case of most of the local banks, a maximum of about a third of rated exposures falls within a single

grade. All three of the overseas comparison studies, two of which are able to be depicted in the graph,



0

1

2

3

4

3 4 5 6 7 8 9 10 16Number of grades

Nu

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anks

9 The rating scales for both of these agencies comprise 10 main ‘alpha’ grades (including a default category) or 22 grades including ‘+/-’ modifiers. As some of the lower agency grades tend to fall outside the banks’ stated normal underwriting standards, some of these grades may either be dropped or grouped together when compared with the banks’ internal rating scales.

found similar results. In the case of three of the regional Australian banks, which rely heavily on the

judgement of raters, between 50 and 70 per cent of rated exposures fall within a single grade.

Graph 5: Concentration of exposures in largest risk grade

3.1.4 Point-in-time vs through-the-cycle grading

Institutions tend to be characterised as taking either a through-the-cycle or point-in-time rating approach.

External rating agencies usually state that they take a through-the-cycle approach, ie a borrower’s rating

is meant to stay the same over the course of an economic cycle unless the firm experiences a major

unexpected shock (good or bad) to its perceived long-term condition or the original downside scenario

used to rate the borrower proves to have been too optimistic. Under this approach, observed default

frequencies for particular ratings will tend to vary with the economic cycle.

Banks, on the other hand, including those in Australia, usually say they take more of a point-in-time

approach, ie they base their ratings more on the borrower’s current condition so that a borrower is more

likely to migrate up and down through the ratings as economic conditions change. Under this approach,

observed default rates for particular ratings should tend to be more stable over the economic cycle.

These differences can have important implications when interpreting ratings-based information,

particularly when ratings from different sources are used in combination, eg when mapping a bank’s

internal grades to external agency grades. Failure to take such differences into account can potentially



0

10

20

30

40

50

60

70

Less than 20 20-29 30-39 40-49 50-59 60-69 70 or morePercent of exposure in largest category

Perc

ent

of

ban

ks

Basel Committee

Treacy & Carey

Australian Banks

%

produce substantially biased estimates of default probability, distorting internal analyses of loan loss

provisioning requirements, appropriate allocation of credit capital, risk-adjusted performance

and pricing.10

3.2 Operating design features

3.2.1 Exposures rated

In principle, the banks apply ratings to all of their corporate, commercial and other transaction-managed

exposures. In practice, exposures below certain thresholds (typically $100,000 or $250,000) tend not to

be rated on an individual basis. At some banks these exposures are assigned a representative rating on a

portfolio basis, at others the exposures remain unrated.

Portfolio ratings may also be assigned to certain other segments of some banks’ loan books. Usually these

segments comprise relatively homogeneous groups of small loans that tend not to be actively managed at

an individual customer level. Such loans include small leasing exposures and exposures that are primarily

assessed on the basis of available security, such as certain stand-alone property exposures or small business

loans secured over the owner’s residence.

3.2.2 Customer PD ratings

(i) Types of rating processes

As outlined in section 2, banks incorporate a wide range of risk evaluation techniques into their rating

processes. In its survey of internal rating systems, the Basel Committee identified three main categories

of risk evaluation processes depending on the degree of judgement involved:

• statistically-based processes – at a small number of the surveyed banks, a statistical default model

or some other quantitative tool was essentially the sole basis for determining ratings – at least

within certain portfolios;

• constrained expert judgement-based processes – were more common. In these systems, ratings

were primarily model-based but relied more heavily on qualitative inputs, non-statistical (ie

expert judgement-based) models and/or permitted to a limited degree (usually one or two

ratings notches at most) judgemental overrides of model-determined ratings; and



10 Treacy & Carey (1998) provides a good discussion of the potential biases involved. In practice, though, the differences in approach between the external rating agencies and banks might be somewhat less sharp than the above discussion implies. For example, where a bank’s rating system involves a good deal of expert judgement and/or permits judgemental overrides of objective ratings criteria, this could contribute to some ‘stickiness’ in the bank’s ratings. On the other hand, various studies indicate that there is a systematic relationship between agency rating transitions and current economic conditions, particularly among lower rated entities, eg refer Basel Committee on Banking Supervision (2000b), pp 141-4.

• (unconstrained) expert judgement-based processes – more than half of the banks surveyed fell

into this category. Some banks had no formal debt rating models while others had models but

permitted raters considerable discretion to override model-determined ratings based on their

professional judgement.

The Australian scenario is similar to that portrayed for other countries by the Basel Committee study. In

terms of the Basel study, most of the local banks would be classified as having (unconstrained) expert

judgement-based processes. Two of the smallest banks have highly subjective rating systems. The other

banks, over recent years, have introduced a variety of rating assessment tools to assist staff in their rating

determinations, including:

• use of external ratings (where these are available);

• more tailored rating definitions (incorporating more detailed quantitative and qualitative rating

benchmarks, often differentiated by customer size and/or industry); and

• debt rating models.

Several banks (mostly the larger institutions with more complex portfolios) utilise multiple models and

other rating tools; these are either tailored to different parts of the banks’ portfolios or provide different

approaches to rating the same exposures.

The banks’ existing debt rating models include a mix of expert judgement-based and statistical models,

either developed in-house for various segments of the banks’ credit portfolios or purchased from external

vendors. The latter tend to be founded mainly on overseas (mostly US-based) experience, though some

ratings systems developed from locally pooled data are now becoming available.

In more recent times, there has also been a trend towards greater reliance on statistical methods, though

only the largest banks have credit portfolios of sufficient size (and the resources) to develop statistical

models based solely on their own loss histories. Most of the statistical models in use are scorecard-type

models, ie the models utilise historical data on defaulted and non-defaulted borrowers to group (rate)

customers with similar characteristics. A couple of the larger banks have developed such models based

on their own loss experience; others use models purchased from external sources. Several of the larger

banks also use externally developed equity-based models to assist in rating both listed and (large) unlisted

corporate borrowers. These models statistically infer default probabilities from so-called distance-to-

default measures based on borrowers’ liability/asset structures and stock-price volatility data.

Most of the local banks permit judgemental overrides of their rating models; in fact, most emphasise to

their raters that rating models are to be used as tools and should not supplant comprehensive analysis of

exposures for rating purposes. Four banks do not permit overrides (at least by front-line lending staff).



In terms of the Basel study, these banks’ rating systems fall largely within the middle category (ie

constrained expert judgement-based processes) due to the importance of a range of qualitative elements

in their main rating models.

(ii) Key rating risk factors

For those banks that have highly subjective rating systems and/or allow judgemental overrides of model-

based ratings, traditional credit analysis plays the sole/potentially primary role in assigning final ratings to

credit exposures. In these cases, individual raters have wide discretion over what information is

considered and how it impacts on final rating assessments. The pervading credit culture within the

organisation will be an important factor influencing rating outcomes that will be communicated to raters

through such channels as training programs and feedback via mentoring, performance assessment against

key indicators and other oversight mechanisms.11 As mentioned, a number of banks have also sought to

provide more explicit guidance to raters by including various benchmarks in their formal rating criteria,

eg in the form of key indicator comparison tables.

The various model-based assessments cover a wide spectrum of different variables. Leaving aside equity-

based models, most of these variables can be grouped into a few broad categories relating to the obligor’s

financial condition and capacity/cashflow, management quality and industry risk characteristics. Most

models currently in use involve several variables from each of these categories; many also input

information about account conduct (particularly those models dealing with the banks’ smaller customers).

These categories represent key areas of traditional credit analysis. Virtually all models in use also involve

both quantitative and qualitative inputs. The latter often generate a good deal of debate during model

development. Although there is evidence that such data are predictive, subjective inputs are also more

open to interpretation/manipulation and can be less responsive to changed business circumstances.

(iii) Link to quantitative default statistics

Banks attempt to quantify the loss characteristics of their risk grades for a variety of reasons. As discussed

in section 3.2.5 below, by tracking the ex post performance of ratings, institutions seek to test whether

their rating systems are effective in differentiating among exposures with different degrees of risk. By

linking internal rating scales to numerical estimates of PD, LGD and/or EL, credit ratings can also be

turned to a variety of quantitative applications, including in more sophisticated pricing, profitability and

performance analyses, and as inputs in loss provisioning and economic capital allocation modelling.

Seven of the surveyed Australian banks assign quantitative default characteristics to their internal ratings.



11 For example, inconsistent incentives built into an organisation’s oversight structures and culture can weaken the rating process, eg where business volume key performance indicators take precedence over quality indicators or risk-based performance indicators are not supported by strong quality assurance processes.

The three banks that do not assign quantitative default characteristics to their internal ratings, possess

rating systems which are not well suited to establishing quantitative default estimates. Given the nature

and small size of their commercial lending portfolios, these banks currently see limited benefits from

introducing more sophisticated analysis techniques.

In line with overseas experience, Australian banks currently lack long-term data on the performance of

their internal rating systems. Like their overseas counterparts, the local banks have sought other ways of

assigning quantitative default (and other credit migration) probabilities to their internal risk grades. In

some cases, average expected default frequencies for each risk grade are based on the default rates

predicted by the banks’ statistical debt rating models. In other cases, internal risk grades are mapped to

the rating scales of longer-established external rating schemes for which long-term performance data are

available (ie Moody’s and/or Standard & Poor’s). Some banks use a combination of both methods.

Mapping to external benchmarks is not straightforward and requires considerable judgement on the part

of analysts in comparing banks’ internal rating processes with those of the external agencies. This can

involve comparing the definitions and criteria underpinning their internal grades with those of the rating

agencies; analysing the financial characteristics of internally rated borrowers with standard ratios that

characterise the agency grades; and analysing the grades of internally rated borrowers that also have

external ratings.12

In order to facilitate the mapping process, many banks have developed internal rating scales and associated

rating criteria that attempt to mirror those of the external agencies. Overseas, some banks statistically

model the external agencies’ rating processes. The models are then used to rate the banks’ customers. This

latter approach has not been adopted by any of the Australian banks, though the technique has been applied

by some of the banks to examine how closely their internal processes align with those of the external

agencies. Overall, differences in the approaches adopted by different banks have led to a good deal of

variation among their nominal PD scales, even where ratings categories are meant to be broadly equivalent.

Benchmarks based on external experience (whether that of external rating agencies or an externally

developed debt rating model) also give rise to questions about their applicability to a bank’s particular

client base and lending practices. Inconsistencies in such things as rating approach (eg point-in-time vs

through-the-cycle rating), portfolio composition (eg in the size, nature of operations and domicile of

borrowers) or type of borrowing instrument (eg bonds vs bank loans) can lead to substantial bias when

mapping internal grades to externally sourced PD values.



12 In the latter case, however, there can be a good deal of circularity involved when external ratings are also an input in the internal rating process.

3.2.3 Facility LGD ratings

A more mechanical approach is typically taken when assigning LGD ratings than for PD ratings. LGD

ratings are generally assigned in one of three ways:

• on the basis of security coverage ratios – this is the main method used by all banks for most

types of exposures. Security coverage is usually based on a discounted or ‘shaved’ loan to value

ratio, whereby estimated loan security values are discounted by standardised ‘safe lending’ or

‘extension’ margins.The discount factors seek to cover such factors as uncertainties inherent in

the valuation process (including liquidity considerations) and potential subsequent downturn in

security values.The banks’ policy documents set out standard valuation procedures and discount

factors based on types of security. The ‘discount factors tend to be based largely on management

judgementand traditional industry benchmarks/‘rules of thumb’. The smallest banks tend to

extend value only on real estate or high quality assets, such as cash and government securities,

whereas larger banks recognise a wider range of security types in their rating procedures;

• by directly estimating an expected recovery percentage – eg in the case of impaired assets; or

• by applying a generic classification based on the type of exposure – some banks use this

approach for certain types of exposures, eg in the case of subordinated debt, small leasing

exposures, exposures secured over residential or non-specialised commercial property and

exposures to other banks.

Currently, five Australian banks seek to assign quantitative LGD values to their credit exposures. Usually

this is based on LGD ratings but in some cases also depends on the product type or line of business.

As in other areas of credit risk rating, this aspect of the banks’ systems is heavily constrained by data

limitations. Although most of the local banks are working to reduce some of these limitations, key LGD

parameters are currently a mix of the banks’ own loss histories (mostly restricted to data on Australian-

based customers and covering a limited time span), published studies of bond and commercial loan

experience (mostly data on US-based customers) and a large degree of management judgement.

Where banks have undertaken LGD studies, methodological differences have arisen. For example, while

some banks base LGD estimates on discounted cash flows and the inclusion of workout costs, others

exclude these factors (usually because the required historical data are unavailable or not easily accessible).

The choice of an appropriate basis for discounting delayed cash flows can also affect the results of these

studies; while some banks favour discounting on the basis of contractual rates of interest, others favour a

discount rate that is related to the cost of equity.



3.2.4 System oversight and control processes

Apart from debt rating models, banks in Australia and overseas incorporate a range of other features into

their rating processes designed to enhance the accuracy, integrity and consistency of ratings throughout

their operations. Generally speaking, these measures seek to reduce the degree of subjectivity and/or

inject greater independence into their rating procedures. Some of the main control features include:

• at all banks surveyed, broad ownership of the rating system resides with an independent central

credit management unit that reports to the bank’s Board/Board Risk Management Committee.

Responsibility for overall oversight of the system’s operation falls to this unit. While business

lines might suggest system modifications, this unit is responsible for approving changes to the

system, with major changes also requiring approval from the Board/Board Risk

Management Committee;

• the assignment of credit ratings is integrated into the banks’ normal credit approval/review

processes and is subject to the checks and balances built into those systems. Typically:

– while relationship managers retain the main responsibility for accurately assigning ratings,

credit approval/review policies and processes channel larger and other riskier13 credit

exposures into the bank’s independent credit line and/or to higher or specialist approval levels

within the credit line;

– at banks where overriding is permitted, a higher approval level is required where

lending/credit officers propose modifying the rating recommendations of the bank’s rating models;

– credit assessments and related ratings are subject to formal periodic review, at least annually

in most cases. Most banks seek to enhance the timeliness of credit reviews, and any associated

rating adjustments, by also specifying early review events and/or more frequent periodic

reviews for lower-rated exposures. At some banks, early review policies are supported by

centralised and/or automated monitoring systems;

• among other aspects of credit risk management, ratings at most of the surveyed banks are subject

to regular and ad hoc review by independent credit review teams, with reporting of main trends

to top management levels within the banks and to board risk management and internal audit

committees. Particular exposures are reviewed on a sample basis, with higher proportions of

loans for review typically being drawn from riskier grades and areas of growing concentration.

Poor credit process ratings received from these review teams can lead to a reduction or



13 For example, at some banks sole lending delegations are substantially reduced in the case of new customers (particularly those seeking to refinance existing loans from other institutions) or existing customers that are branching into new fields of activity.

cancellation of delegated credit authority and, at several banks, can directly affect

staff remuneration;

• several banks also test the performance of their rating systems, including through regular

monitoring of credit migration data against expected outcomes.

In addition, some banks have established, or are considering establishing, automated data transfer linkages

to minimise/eliminate rekeying of ratings input data (eg financial ratios from financial spreading packages)

and of completed ratings. The aim is to improve system efficiency, including by reducing inconsistencies

in different data management systems caused by transcription error, failure to update databases or

potential manipulation of ratings information. Some systems also seek to track potential instances of

‘gaming’ rating models whereby loan officers might alter customer information and re-enter it several

times in order to obtain a better rating recommendation. As a further means of enhancing rating

consistency, efficiency and overall accuracy, at some banks where industry characteristics form an

important input into rating models, an economics or other specialised unit, rather than individual

lending/credit officers, is responsible for inputting relevant industry assessments.

A couple of banks regularly undertake centralised monitoring of model override trends. Such

monitoring can help indicate potential problems in the way rating models are being used within a bank

and/or deterioration in model performance. That said, overrides are not ignored at other banks, as the

review of override decisions is an important aspect of the responsibilities of the banks’ credit line and

credit review teams.

3.2.5 Validation procedures

On-going testing of the system’s ability to distinguish accurately the riskiness of different exposures is an

important element of any credit risk rating system. It also requires a good deal of subjective judgement

(given the long-term nature of credit cycles and short-term record of existing bank systems). Validation

procedures can involve comparing evolving credit migration statistics against expectations and/or

comparing internal ratings with other available rating alternatives, eg external agency ratings and/or

externally developed rating models.

These procedures aim to:

• confirm the continuing ability of the system to rank risk effectively;

• test the accuracy of quantitative PD estimates (where these have been assigned to risk grades);

and/or

• compare the discriminant power of alternative rating schemes.



The larger Australian banks regularly review credit migration data and from time to time undertake

comparison studies of alternative rating systems. While the various analyses of credit migration data

suggest that the banks’ rating systems can rank risk in a relative (or ordinal) sense, the accuracy of their

associated quantitative probability scales remains more problematic given that available data have only

been drawn from the good part of the current credit cycle. Most of the smaller banks are currently

building up their internal default histories but to date have gathered insufficient data to form valid

conclusions as to the efficacy of their rating systems.

3.3 Rating applications

As outlined earlier, the applications for which an internal rating system is utilised provides an important

indication of the degree of sophistication that should be built into the system’s design and operating

features. Banks that utilise internal ratings for relatively sophisticated purposes (eg as inputs in statistically-

based provisioning, capital allocation and risk-based pricing models) require more sophisticated rating

systems that feature adequate risk differentiation, strong control frameworks and ratings that are linked to

quantitative default characteristics. However, where used for less demanding analytical tasks, a less

sophisticated system may be adequate.

Australian banks’ internal risk ratings are used in varying degrees in a wide range of applications which,

on an industry basis, cover the full scope of administrative, reporting and analytic uses outlined in

section 2.3.

Table 2: Administrative uses

For example, the larger banks utilise their internal risk grading systems to assign delegated credit approval

authorities to lending personnel. At these banks, the maximum amount that each lending/credit officer

may approve for any particular obligor varies by risk grade; ie delegated lending authorities are larger for

less risky grades and vice versa. Use of this technique affords the banks greater flexibility in tailoring

lending delegations to the skills and circumstances of particular lending officers. Among the smaller

banks, ratings are used more simply in the delegation process. Typically, lending staff are prohibited from

authorising new lending below a certain threshold rating; above that threshold, lending authority is

typically restricted to a fixed dollar amount irrespective of the obligor’s rating.



Delegated credit approval authorities 5

Problem loan management 10

Number of Banks

All of the Australian banks surveyed also use their rating systems to facilitate problem loan management.

Typically, when an exposure is assigned certain (low) grades, it becomes subject to requirements for:

• more frequent monitoring and reporting on the condition of the obligor and prospects

for repayment;

• the development of a formal rehabilitation or exit strategy; and/or

• transfer to a specialist asset management unit.

Table 3: Analytic uses

All of the Australian banks utilise ratings for portfolio monitoring and management purposes. Among

other things, the banks’ internal rating systems are used to report to high management levels total asset

balances, large exposures and relative change in distributions for each risk grade. This information

provides management with analyses of the mix of loans within the banks’ portfolios and various sub-

portfolios (including by business line, industry and product type), data on problem assets and the risk

profile of assets within pass grades. Ratings are also used to communicate risk-differentiated business

acquisition strategies, eg in developing customer target profiles for particular products.

Four banks have developed statistical provisioning and credit risk capital modelling capabilities. Output

from these models, which incorporate credit ratings as major inputs, is used for a variety of purposes,

usually including:

• benchmarking total capital and general provisioning requirements;

• internal capital allocation;

• internal risk reporting;

• portfolio management;

• the setting of credit risk concentration limits;

• risk-adjusted profitability measurement (using risk-adjusted return on capital and shareholder

value added concepts); and

• developing risk-based pricing benchmarks.



Portfolio management/monitoring 10

Provisioning/capital allocation modelling 4

Risk-based pricing 10

Number of Banks

Some of these banks are also beginning to use the information to affect performance-based remuneration

in some parts of their businesses.

4 CONCLUDING REMARKS

The preceding sections of this report have outlined the current state-of-play regarding credit risk rating

among Australian banks and provided some international comparisons. Generally speaking, Australian

banks’ credit risk rating practices appear to be in line with those of their international peers.

Internal bank rating practices, both locally and overseas, continue to evolve as the experience of rating

institutions builds. As a group, the local banks have moved relatively quickly to adopt two-dimensional

credit risk rating approaches whereby customer default probabilities and expectations of loss in the event

of default are separately rated. In other respects, as elsewhere in the world, considerable differences exist

among the banks’ rating systems, particularly in relation to the detail of how ratings are determined, the

associated quality control processes that have been established in each institution, and the way in which

quantitative values have been assigned to risk grades.

These differences reflect many influences deriving from the particular circumstances of each institution,

including differences in the size and nature of banks’ rated portfolios, the intended applications of ratings,

the capabilities of banks’ available resources and systems, the legacy of past decisions, and costs of change.

As noted elsewhere in the paper, after weighing the various considerations involved, no single ratings

approach is necessarily right for all institutions or for any particular institution over time.

A key challenge faced worldwide by virtually all developers and users of internal credit risk rating

systems, including prudential supervisors looking to utilise banks’ internal ratings for regulatory capital

and other purposes, is the widespread lack of good long-run data on the performance of banks’ loans.

The lack of such data can impact on the ability of an institution to develop effective rating tools. It can

also impede efforts to verify the accuracy and robustness of institutions’ rating systems, assign reliable

quantitative loss estimates to risk grades, and make reliable comparisons of ratings from different

institutions: all important tasks not only from the perspective of banks, themselves, but also their

prudential supervisors (particularly in the context of proposals to utilise banks’ internal ratings for

regulatory capital purposes).

While many banks have implemented data warehousing processes aimed at improving this situation, given

the length of a typical credit cycle, it will be some years yet before these banks have data covering one

full credit cycle let alone several different cycles. Moreover, irrespective of the stage of the credit cycle,

institutions with small rated portfolios may not experience sufficient defaults for many years to come, if

at all, before any reliable statistical analysis of their default experience or the performance of their rating

systems can be undertaken.



Other means by which banks have sought to overcome data deficiencies include industry pooling of

default data, use of other external benchmarks and externally developed rating models. Data pooling and

externally-developed models can be of particular potential benefit to smaller banks, given their generally

more limited data and other resources, and their narrower exposure to the risk spectrum. As discussed,

however, use of external resources also exposes institutions to risks relating to the applicability or

otherwise of the external measures to their individual customer bases and lending practices, and whether

the external benchmarks, models etc will continue to exist and be appropriately maintained.



APPENDIX 1: TYPES OF CREDIT RISK RATING MODELS

Credit risk rating models can be broadly classified as either being based mainly on expert judgement or

statistical analysis.

Expert judgement- or rules-based models are non-statistical models that have been developed on

the basis of professional judgement. The models can take the form of decision trees or, more frequently,

scorecard-type models where both the input variables and associated coefficients are judgementally

derived based on the knowledge and experience of an organisation’s expert lending/credit officers.

Where sufficient data are available, statistical models may be developed utilising a variety of techniques.

For example, some overseas banks seek to model the decision processes of the major rating agencies by

regressing various financial ratios and other variables against the agencies’ ratings. More frequently,

statistical techniques are used to analyse differences in the characteristics of large groups of banks’

defaulted and non-defaulted customers. Usually, one of three basic approaches is adopted:

(i) linear probability models – these models are developed by regressing a selection of quantitative

and/or qualitative variables describing borrower characteristics against a (dichotomous) dependant

variable that takes a value of 1 if a loan is in default or 0 otherwise. One problem with such models

is that the estimated default probabilities are not guaranteed to lie within a range of 0-1. That said,

linear probability models remain the most common means of credit scoring/rating consumer and

other retaillending products;

(ii) logit/probit models – utilise more sophisticated regression techniques that constrain estimated

default probabilities within a 0-1 range. This is achieved by assuming that default probabilities are

distributed in particular ways within that range (eg probit models assume that default probabilities

are normally distributed while logit models assume that a logistic distribution is more appropriate).

Logit and probit models have become more common with increased computing power and the

development of statistical debt rating models for commercial and corporate borrowers;

(iii) linear discriminant models – tend to be more commonly found in academic literature. These

models seek to establish a linear classification rule (or formula) that best distinguishes between

particular groups of borrowers (specifically defaulters and non-defaulters). This is achieved by

analysing a range of variables (borrower characteristics) in order to determine a set of coefficients

that maximises the ‘between group’ variance, while minimising the ‘within group’ variance, of a

sample set of defaulting and non-defaulting borrowers. Linear discriminant models assume that

borrowers belong to discrete categories, unlike the above-mentioned probability models, which

envisage a graduated spectrum of borrowers from low to high risk.



Some other statistical techniques, such as artificial neural networks and genetic algorithms, have an ability

to ‘learn’, ie these techniques can automatically adjust an institution’s rating models as new default data

come to hand that indicate underlying relationships between the characteristics of borrowers and

propensities of borrowers to default are changing or could be better specified. Although several Australian

banks use the techniques for various other purposes, eg potential fraud detection, none of the banks

currently use neural networks or similar techniques for credit risk rating purposes. Similarly, we are not

aware of these techniques being used for ratings purposes overseas, except possibly at a very few banks or

on an experimental basis. One Australian bank has indicated that it is contemplating using neural

networks as a diagnostic check of its existing rating models rather than as its main rating tool.

Some banks also use equity-based or option-theoretic models (usually purchased from external vendors)

for relevant parts of their portfolios. These models use information contained within a borrowing firm’s

share price as an indication of credit risk. US-based KMV Corporation is the main supplier of these

models globally. Moody’s Risk Management Services has also recently developed a public firm rating

model, which takes a hybrid approach that combines a standard probit model with

option-theoretic concepts.



APPENDIX 2: EXAMPLE TWO-DIMENSIONAL CREDIT RISK RATING MATRIX

This hypothetical matrix (or look-up table) illustrates how a 9-grade composite credit risk rating scale

might be derived from an 8-grade PD rating scale and 7-grade LGD rating scale.



Customer Facility LGD ratingPD Rating

1

2

3

4

5

6

7

8

A B C D E F G

1 1 1 2 2 3 3

1 1 2 2 2 3 3

2 2 3 3 3 4 4

3 3 4 4 4 5 5

4 4 5 5 6 6 6

5 5 6 7 7 7 7

7 7 8 8 8 8 8

8 8 9 9 9 9 9

References

Basel Committee on Banking Supervision (1999), A New Capital Adequacy Framework, June,

(www.bis.org).

Basel Committee on Banking Supervision (2000a), Range of Practice in Banks’ Internal Rating Systems,

January, (www.bis.org).

Basel Committee on Banking Supervision (2000b), ‘Credit Ratings and Complementary Sources

of Credit Quality Information’, Basel Committee on Banking Supervision Working Paper 3, August,

(www.bis.org).

Eales, R (1997), ‘Credit Risk in Corporate Banking – Theory and Practice’, Credit Risk in Banking:

Proceedings of a Conference, Reserve Bank of Australia, May.

English,WB & Nelson,WR (1998), ‘Bank Risk Rating of Business Loans’, Working Papers: Finance &

Economics Discussion Series, Board of Governors of the Federal Reserve System, November,

(www.federalreserve.gov).

Treacy, WF & Carey, MS (1998), ‘Credit Risk Rating at Large US Banks’, Federal Reserve Bulletin,

November, (www.federalreserve.gov).



NOTES



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