DOI: 10.5817/FAI2017-2-5 No. 2/2017
61
Banking Rating Systems for IRB: Comparison of Three
Systems from German-speaking Environment
Martin Svítil
Soukromá vysoká škola ekonomická Znojmo
Loucká 656/21, 66902, Znojmo
E-mail: svitil.martin@svse.cz
Abstract: The aim of the article is to compare several rating systems used by
banks and their affiliates, especially in German-speaking countries. The research is
focused on the rating of business entities, more precisely the corporate ones,
(especially limited liability companies or joint-stock companies). In particular, two
aspects of the rating of the corporations are highlighted in the rating system
comparison, namely: (i) which quantitative indicators are used for the calculation
of the hard-facts and (ii) how the soft-facts are included in the rating system. The
result of the research shows, that concerning the quantitative indicators (i), the
highest emphasis is put on the capital structure of an enterprise (whether in the
form of Equity to Liabilities or Equity to Total assets) in all compared rating systems.
Concerning the other indicators used for the calculation, the monitored rating
systems are different, and the total number of indicators also differs (from 6 to 9).
For soft-facts (ii) all rating systems agree on some of the queries (e.g. the
sensitivity of the rated entity to market fluctuations), but otherwise the number
and the topics of the queries overlap only partially.
Keywords: hard-facts, rating, risk management, soft-facts
JEL codes: G11, G21, G24, G32
Introduction
Internal rating systems are currently an integral part of the risk management
system of banks and non-banking financial institutions. In addition to the original
purpose, which is the assessment of clients' creditworthiness, they also play an
irreplaceable role in determining the capital adequacy of banks in compliance with
the Basel II accord and the emerging Basel III accord respectively. For more
information about the Basel accords please refer to Basel II (2006) or BIS (2010).
This article deals with the comparison of the internal rating systems used currently
and / or in the recent past by three banks or banking groups originating from the
German-speaking economic environment and regions. All the banks mentioned
apply the IRB Approach for Basel II and use their rating tools to assess the credit
risk of their clients, expressed in the PD (probability of Default).
As the issue of rating systems is quite extensive, I focus only on selected matters
of the rating systems mentioned here, namely: (i) which quantitative indicators are
used for the calculation of the hard-facts (quantitative analysis based on data from
reports, especially balance sheets and profit and loss accounts); and (ii) how the
soft-facts are included in the rating system (qualitative indicators usually based on
a questionnaire or interview).
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1 Review of Literature
A similar topic, but from different point of view was studied by Bakhtiari (2017).
His paper focuses on the Standard and Poor's (S & P) credit rating of firms in the
USA. Bakhtiari tried to find positive or negative correlation between the credit rating
and productivity of companies. The findings of the paper point to size as to one
major balancing factor for a good rating result. With a focus on manufacturing, the
evidence points to two types of companies getting an investment grade rating: (i)
medium-sized productive firms (firms “driving the creative destruction” according
to Bakhtiari) and (ii) very large but not so productive firms (too-big-to-fail and
“resisting the creative destruction”). The Bakhtiari’s results suggest that the
substitutability of size and productivity in the ratings system is not recent and has
been around for a long time. In this context, the effort to find the most important
factors for the rating results and the potential change of the weights of these factors
with size of the rated company makes sense. The size of the rated company (bank's
client) and its importance for the rating result is one of the topics I deal with in this
paper.
Jankowitsch, Pichler and Schwaiger (2007) analyze the potential economic value of
improving the credit rating system. They show, that an investment in the rating
system can bring not only a reduction in Basel's required capital held in the bank
but also a reduction in credit losses by increasing the annual rate of return by 30–
40 bps. Their results demonstrate "the significant potential economic value to a
lender of better rating systems because these ratings allow the banks to price loans
more effectively." It is questionable, how far can the banks in high-competitive
environment on current market really adjust the prices of their loans by risk - since
2007, many things have changed, especially because of the financial crisis. But the
more effective rating system and the following better risk evaluation is surely an
advantage by all means and in any environment, as stated e.g. by Spuchľáková,
Valašková, Adamko (2015): "The effective management of credit risk is a critical
component of a comprehensive approach to risk management and essential to the
long-term success of any banking organization."
One of the most recent as well as geographically closest researches about credit
rating is the work of Weissova, Kollar and Siekelova (2015), who deal with the
situation in Slovakia. Their paper shows very similar structuring of the rating
system(s) to hard- and soft-facts (see below), but they do not deal with more
detailed analysis of the indictors used in these categories.
Berg and Koziol (2017) in their paper, covering 40 banks and 17,000 corporate
borrowers from 2008–2012 in Germany, found three main results, related to banks’
rating systems and their results. (i) First, the variability of PD estimates for the
same borrower across banks is large. (ii) Second, bank fixed effects explain 5% of
the variation in PD estimates across banks, while 95% of the variation is
idiosyncratic (non-systematic and thus not affecting capital requirements). (iii)
Third, there are various bank characteristics that explain the size of bank fixed
effects, like that weaker-capitalized banks on average report lower PD estimates
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and that banks’ reported PD estimates increase after significant capital increases.
But as the authors emphasize, their results should be interpreted with care.
For the basic overview and historical background about credit rating agencies (I
mention the agencies as source for external ratings in point 3.2 and use the Moody’s
scale for comparison in point 3.5) the paper of Jeon and Lovo (2013) can be
recommended.
Danielsson (2002) and Danielsson, Song Shin and Zigrand (2004) show on the
example of market risk and value-at-risk models (VaR), that if many players on the
same market use the same or similar risk models, it could have very negative
consequences. If many market participants apply the same or very similar strategy
at the same time, it can make the fluctuation bigger and the situation worse.
Although this study covers market risk and value-at-risk models, the degree of
similarity can be an interesting question in different types of models too.
The most authors mentioned above use more statistical point of view, thus they
usually do not analyze the internal structure of the rating systems / tools. I try to
answer the question, how far the credit risk models (banks’ internal rating models)
are similar and what are the most important factors of rating results, with
awareness of limits of available data (regarding the region, the number of
monitored banks etc).
Fracassi, Petry and Tate (2016) were concerned with interesting question of how
rating analyst subjectivity affects corporate debt pricing. In my paper, I can only
briefly mention the soft-facts part of the rating systems (where there is the only
potential space for rating analyst subjectivity), but I see this topic as possible
direction for future studies.
2 Data and Methodology
For comparison, documentation to the three rating systems used by three different
banks (banking groups) from the German-speaking environment was available. The
data from years 2004 up to 2016 were used, where the most important documents
(rating manuals, scales etc.) are dated 2004, 2008, 2011 and 2016.
Since some of these documents are confidential and have not been granted full
disclosure, the results of the investigations are limited in some respects. It is also
not possible to publish the names of the mentioned banks (banking groups). The
data are anonymised accordingly, but this is done in a way which does not affect
their value to the research. These three banking institutions and their rating
systems are listed under A, B and C. Due to the sensitivity of both client and own
bank data, it was not possible to obtain more detailed information or even
numerical data on outputs of rating systems. Therefore, the paper is more focused
on the comparison of the rating methods and rating systems, not on numerical
results.
In all cases, the rating tools are used in a form of a software tool for Microsoft
Windows OS. The empirical data was collected and assessed according to the
64
criteria of verifiability and relevance. Subsequently, the induction method was
applied and conclusions were generalised.
3 Results and Discussion
3.1 Scopes of Rating Systems
The research is focused on the rating of business entities, more precisely corporate,
(especially limited liability companies or joint-stock companies). It does not take
into account the rating of natural persons or non-profit organizations, municipalities
etc.
Nevertheless, the criteria and limits for the decision, which module (or rating tool
included in the rating system) should be used for particular client, are not such
simple and differ from bank to bank slightly.
Bank A uses two criteria for the enterprise entities, as the Table 1 shows: (i)
operating revenue (consolidated, if applicable) and (ii) group exposure (according
to Basel II rules, which means total of all credit lines without term loans plus original
amount of outstanding term loans plus account balances exceeding credit lines).
For (i) operating revenue the limit is set to EUR 50 million, for (ii) group exposure
to EUR 1 million. If the client exceeds at least one of the limits, the bank uses the
Corporate rating tool for him. If not, the Retail rating / Scoring tool is used, as the
following table shows.
Table 1 The bank A criteria for decision between Retail / Scoring
and Corporate rating tools
Consolidated operating revenue
≤EUR 50 million > EUR 50 million
Group
exposure
≤ EUR 1 million Retail / Scoring Corporate rating
> EUR 1 million Corporate rating Corporate rating
Source: Author by using bank “A” rating manual
Besides these rules, for some other types of enterprises the Corporate rating tool
is used automatically, regardless of the quantitative criteria mentioned above. This
is the case for holding companies, real estate private (except for non-reporting
companies), non-profit housing construction companies, insurance companies,
political parties and non-banking financial institutions. By contrast, for the banks
there is a different special rating tool.
Bank B does not use quantitative criteria for the decision of using the corporate
rating tool. The rating in Corporate tool should be always based on audited financial
statements or at least on data with "certified expert's confirmation of conformity".
Special different tool is used for financial companies like banks, leasing companies,
insurance companies etc.
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Bank C does not use the quantitative criteria either, but its rating system has more
different tools, designed for particular groups of clients like start-ups (new
companies with no full year financial results), real estate companies / projects and
financial companies (banks, insurance companies). The request for audited
financial statements for the Corporate rating tool is set, too.
3.2 Basic Structure of Rating Systems
The basic structure of all the three rating systems mentioned is quite similar. All
three compared rating systems first produce the part of the rating, based on clients’
financial statements (financial data). After possible other inputs like sector data,
budgets etc., the financial statement rating becomes quantitative or hard-facts
(HF) rating, as one of the two main sources for the final result. The second source
is the qualitative or soft-facts (SF) rating, based on qualitative criteria, where
usually the key account manager provides most of the information used (for details
of the quantitative and qualitative indicators used see below - points 3.3 and 3.4).
In the next stage, the hard-facts rating and the soft-facts rating are integrated
together, according to a given weighting formula. This formula differs slightly at
the compared rating tools, reaching from 66 : 34 (HF : SF) up to 50 : 50. Two
rating systems (banks A and B) use fix weights for all their corporate clients, the
rating system of the bank C is more complex, with the weights varying with a
change of the size of the rated company. The more turnover the rated company
makes; the more weight the hard-facts have in the final rating. This is a logical
solution: smaller companies are more endangered by one person’s mistakes or loss
(like death of the owner / CEO in one person), their processes (like accounting etc.)
are usually less intensively checked by internal and external audits etc. The weights
in the bank's C system start with the 50 : 50 ratio for companies with annual
turnover under EUR 1 million and continue with increasing importance of
quantitative (hard-facts) indicators up to 66 (HF) : 34 (SF) ratio for companies with
turnover over EUR 75 million a year. Using the weights, the hard-facts rating and
the soft-facts rating result together in an automatically calculated or standalone
rating. From this point on, the rating systems start to differ.
Bank A considers first the age of the financial statements, used for the calculation.
If they are older than 18 or 21 months respectively (closing day of the latest
financial statement), the rating is downgraded by two or three grades respectively.
If the dates are older than 24 months, the rating is automatically downgraded to
grade 7 (the worst non-default grade). After this step, the rating becomes
Recommended rating.
It has to be mentioned, that the other two banks (B and C) take the age of the
financial statements in consideration too, but in different ways. The bank's B
system just does not allow to make a rating from financial statements older than 3
years. In case the statements are between 2 and 3 years, the system shows a
notice, but does not change the rating result. For the bank C, the financial
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statements older than 21 months are one of the risk factors, considered in the
rating process (see below).
Next step in the bank's A rating system are the possible downgrades due to
negative information like high dependency on suppliers and / or customers, high
write-downs, negative business prospects etc. This information can make the rating
worse by several grades or downgrade it directly to the grade 7.
The last possible change of the rating comes with up- / downgrading or even
overruling of the client's rating by using the external credit rating (made by one of
respected agencies) or group's, mother's or guarantor's rating - the particular rules
are given. When this last step is made or skipped, the rating becomes Final
customer’s rating. The basic structure of the bank A rating system is shown
Figure 1.
Figure 1 The basic structure of the bank A rating system
Source: Author's illustration based on Bank A rating manual
In the case of bank B, the external rating could be used too, but only if the
customer's shares are traded on stock exchange. In such a case, the external rating
influences the hard-facts rating.
In following steps, the basic structure of the rating system is similar to bank A,
except that the possible up- / downgrades are made just in one step, making Stand
- alone rating to Final customer’s rating. The basic structure of the bank B
rating system in shown Figure 2.
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Figure 2 The basic structure of the bank B rating system
Source: Author's illustration based on Bank B rating manual
The bank C uses the variable weighting formula for hard- and soft-fact (as
mentioned above), in following steps there is one interesting difference between
banks A and B. Generally, the possible manual down- or upgrades in ratings (in all
monitored banks A, B and C) can be made either by any person of the risk
management team with the supervision of team head or can be made by chosen
person(s) only. The four eye principle (maker - checker) is always kept. But only
in the bank's C rating system, after the Automatically calculated rating is made,
the relationship manager / salesman has the opportunity to manually downgrade
the rating by selecting the risk factors / warning signals. There is no way for the
relationship manager / salesman to upgrade the rating and any change has to be
approved by a member of risk management. The possible reasons for downgrade
can be for example the age of the financial statement (more than 21 months), high
amount of doubtful receivables (not noticed in the financial statements, but known
to the bank), restructuring non-trustworthy financial statements etc. Obviously the
idea is that the salesperson knows the client better because of the personal contacts
and visits than a risk manager does.
Then follows the possible correction of the rating by risk management and possible
overruling by mother company's rating. The result is the Final customer’s rating.
The basic structure of the bank C rating system is shown the Figure 3.
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Figure 3 The basic structure of the bank C rating system
Source: Author's illustration based on Bank C rating manual
3.3 Quantitative Indicators (Hard-facts)
For comparison of quantitative as well as qualitative indicators, the overview
scheme including all three rating systems was made by author of this paper. In
some cases, the weight applies for two or three indicators together, which is
represented by the merged and centered particular column in the scheme. Please
note that the numbers of weights are rounded.
As the Table 2 shows, regarding the quantitative indicators (hard-facts), the highest
emphasis is put on the Capital structure of the rated company. This applies to all
three rating systems compared, whether in the form of Equity to Liabilities or Equity
to Total assets. Two rating systems pair the Equity ratio with other one or two ratios
respectively, concerning Liabilities.
There are three other categories of quantitative indicators (hard facts), used in
specific form in all three rating systems. These indicators are (i) Cash Flow to
Liabilities (in two cases together with Income to Interest in slightly different form),
(ii) Profit to Sales (in one case together with Profit to Total Assets) and (iii) Trade
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payables and Notes payable to Sales; all these indicators have important, but lower
weight than the capital structure of the company.
Table 2 Quantitative indicators (hard facts)
Note: The numbers of weights are rounded.
Source: Author' illustration based on banks “A”, “B” and “C” rating manuals.
In two cases, the indicators for Growth and for Liquidity are used, in slightly
different forms. For the rating system B, where the most indicators (nine) occur,
the unique one measures Personnel expenses against Sales with 10% weight
(rounded).
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3.4 Qualitative Indicators (Soft-facts)
The Comparison of qualitative (soft-facts) indicators is made by high-level
categories, because of keeping the scheme uncluttered. The extent of this paper
does not allow to perform a deeper analysis of this topic, too.
It should be mentioned, that in every high - level category (topic) more particular
questions / opinions are concentrated. Answering of the questions and making of
the soft-facts rating is usually a result of collaboration of key account manager /
relationship manager / banker (which can be just different names for very similar
positions in the banks) and the member of risk management team or Chief risk
officer respectively. In all cases, the four-eye principle is kept, with respect to
possible subjectivity of both involved persons, especially the key account manager
/ relationship manager / banker (who usually gets benefit for every deal made).
Anyway, the scheme shows apparently, that all the rating systems agree on some
of the categories, but the similarity is lower than by quantitative indicators and the
topics of the queries overlap only partially. The Table 3 shows the qualitative
indicators of all three rating systems.
Table 3 Qualitative indicators (soft-facts)
Note: The numbers of weights are rounded.
Source: Author' illustration based on banks “A”, “B” and “C” rating manuals.
An interesting difference to mention is surely the category Relationship with the
bank, which includes the period as well as the quality of the collaboration between
the bank and the customer (thus the rated company). One of the rating systems
take this indicator as the most important one with the weight of 40% (rounded),
the second one uses this indicator with the 10% weight and the third one does not
use this indicator at all (includes only problems in the relationship / previous
collaboration with the customer as a warning signal, if existing). Even the later
approach can make a good sense, considering the result of the rating process as a
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universal transferable number (which can, but does not need to be relevant in this
situation). If the indicator "Relationship with the bank" is not applicable (new client
with no history in the bank), the weight is given at 0 and the weights of the
remaining criteria are equally increased - e.g. in the bank A rating system all the
remaining four qualitative indicators get the weight of 25% for a new client.
Another noteworthy difference between the rating systems are the unique
indicators of rating system “B”, concerning the divergence from company’s planning
in last three years and the company’s business expectations including the bank’s
own view and opinion to these expectations.
3.5 Scales of Rating Results
Concerning the scales used for the final rating result, the differences between the
rating systems are rather unessential. This is hardly a surprise, if we take into
consideration that all the rating systems have one common goal: to estimate the
PD (probability of default) of particular customers of the bank between 0 and 100%
for the following year. The rating systems also have to fulfill some given
requirements in order to be accepted as a tool for counting the capital requirements
of Basel II or Basel III respectively.
On the high end of the scale, the banks A and B use rating grades 1-1 and 1-2 or
1(AAAA) respectively for customers, who even do not have corresponding Moody’s
rating and PD. The bank B describes this category as clients with „no credit risk at
all”, which is rarely a real situation. The bank A uses the first two grades just for
sovereigns and other banks, thus even the best rated company clients can only get
the grade 1-3, which corresponds with best grade of Moody’s rating and real PD.
Similar situation exists at the other two banks: the best company clients can reach
he rating grade 1(AAA) or 1A respectively, but even such a result is more of
theoretical case.
On the lower end of the scale, all the banks use special grades for default clients,
corresponding with Moody's grade C. The bank A uses as much as eight particular
grades (8-1 to 8-8) for separate reason for default (like insolvency proceeding,
overdue payments, restructuring etc.). An experienced user of the rating system
can thus easily see the reason for the default grade of the rating. The bank B
distinguishes three default grades (16, 17 and 18) by the estimated next progress
of the client's situation. One grade represents the situation where the bank believes
in possible recovery of the transaction after restructuring. In the next grade the
possible recovery is described as unlikely. The last grade is a definitive default of
the transaction and the client, with no hope for recovery. The bank C use a method
similar to bank A, but there are five grades of the default rating only. Another
common feature for all three banks and their rating systems is, that the default
rating grade (or more precisely any default grade) can be achieved only by existing
risk factors (warning signals) or signs of default, but never by bad financial results
or financial indicators only. The worst possible rating grade by financial statements
only without any warning signals is 7 or 15 or 4E respectively.
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The Table 4 shows the scales used by all three mentioned banks, compared with
the Moody's rating scale.
Table 4 The scales of rating results
Rating A Rating B Rating C Moody's
Indicative PD in 1
year
1-1
1 (AAAA)
1-2
1-3
1(AAA)
1A
Aaa
0.01%
2-1 Aa1
2-2 1(AA+) Aa2 0.02%
2-3 1(AA) 1B Aa3 0.02 - 0.03%
3-1 1(AA-) 1C A1 0.03% - 0.04%
3-2 1(A+) 1D A2 0.05% - 0.06%
3-3
1(A) 1E
A3
0.07% - 0.08%
1(A-) 2A 0.08% - 0.09%
4-1 2 2B Baa1 0.1%
4-2
3 2C
Baa2 0.1 - 0.2%2D
4 2E
4-3 5 3A Baa3 0.2% - 0.5%
5-1 6 3B Ba1 0.6% - 0.7%
5-2
7
3C Ba2 0.8% - 1.6%
8
5-3 9
3D
Ba3 1.6% - 2.4%
3E
6-1
10
4A B1 2.4% - 5.4%
11
12
4B B2
5.4% - 12.2%
6-2 13
6-2 14 4C
B3 12.2% - 17%
4D
7
15 4E
Caa 1-3
17% - 100%
7 Ca
8-1 - 8-8
16 5A
C Default
5B
17 5C
5D
18 5E
Note: The PDs are rounded.
Default (defined according to Basel II) = 90 days past due or unlikely to pay.
Source: Author by using banks A, B and C rating manuals
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Conclusions
The result of the research shows, that the structures of compared bank’s internal
rating systems are quite similar, based on integration of hard-facts rating based on
quantitative indicators and soft-facts rating based on qualitative indicators, with
possible additional steps according to the client (e.g. existing or non non-existing
guarantor, external rating etc.). My conclusions about hard-facts part of credit
rating systems are in line e.g. with the recent research of Weissova, Kollar and
Siekelova (2015), who deal with the situation in Slovakia.
Concerning the quantitative indicators (i), the highest emphasis is put on the capital
structure of a company (whether in the form of Equity to Liabilities or Equity to
Total assets) in all three compared rating systems. Cash Flow to Liabilities, Profit
to Sales and Trade payables and Notes payable to Sales are other indicators used
universally; the other indicators used for the calculation differ in the systems, as
well as the total number of indicators (from 6 to 9). Most of the indicators
mentioned here appear e.g. in the paper of Van Laere and Baesens (2010),
concerning the rating systems in insurance branch. The relatively high similarity of
the structures of compared bank’s internal rating systems as well as the of the
hard-facts indicators corresponds to the results of Danielsson (2002) and
Danielsson, Song Shin and Zigrand (2004), who, however, focused on models of
market risk and on value-at-risk models (VaR).
The size of the rated company is included under hard-facts calculation just in one
of the three cases mentioned, where the more turnover the rated company makes,
the more weight the hard-facts have in counting the final rating. Thus the size of
the company is not a real hard - facts indicator and the mentioned rating systems
in German-speaking environment currently do not reflect the Bakhtiari's (2017)
conclusions about credit rating of firms in the USA, where the rating systems favour
medium-sized productive firms and very large but not so productive companies.
The relationship between the size of the company, the rating results and the real
PD (especially in European area) can be seen as an interesting topic for following
research.
For soft-facts (ii) all rating systems agree on some of the basic categories (e.g. the
quality of the management, market situation and sensitivity of the rated entity to
market fluctuations etc.), but otherwise the number and the topics of the queries
overlap only partially.
My conclusions can be useful for any bank or financial institution, considering the
development of their own credit rating system, investing in the purchase of an
existing corporate rating solution or, where appropriate, improving or updating the
rating system already in use. As other surveys like Jankowitsch, Pichler and
Schwaiger (2007) show, investment in the rating system can bring not only a
reduction in Basel's required capital held in the bank but also a reduction in credit
losses.
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