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1970-01-01 08:00
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2021年1月23日发(作者:功耗英文)
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英文原文:

A credit scoring approach for the commercial banking sector
Ahmet Burak Emel, Arnold Reisman and Reha Yolalan



Yapi Kredi Bank, Levent, 80620, Istanbul, Turkey.


The Graduate School of Management, Sabanci University, Istanbul, Turkey












































Available online 15 March 2007

The economic and, therefore, the social well-being of developing countries with
fairly
privatized
economies
is
highly
dependent
on
the
behavior
of
a
country's
commercial banking sector. Banks provide credit to sustain anufacturing, agricultural,
commercial
and
service
enterprises.
These,
in
turn,
provide
jobs
thus
enhancing
purchasing
power,
consumption,
and
savings.
Bank
failures,
especially
in
such
settings, send shockwaves affecting the social fabric of the country as a whole and, as
experienced recently, (Latin America and Asia) have the potential of a quick global
impact. Thus, it is imperative that lending/credit decisions are made as prudently as
possible while keeping the decision making process both efficient and effective.

Commercial
banks
provide
financial
products
and
services
to
clients
while
managing a set of multi- dimensional risks associated with liquidity, capital adequacy,
credit, interest and foreign exchange rates, operating and sovereign risks, etc. In this
sense, banks may be considered to be “risk machines”. They take risks, and transform
or embed such risks to provide products and services.
Banks
are also
“profit
-
seeking” organizations
basically formed to
make money
for
shareholders.
In
their
typical
decision-making
processes
(i.e.
pricing,
lending,
funding, hedging, etc.), they try to optimize the
ir “risk
-
return” trade
-off. Management
of
risk
and
of
profitability
are
very
closely
related.
Risk
taking
is
the
basic
requirement
for
future
profitability.
In
other
words,
today's
risks
may
turn
up
as
tomorrow's realities. Therefore, banks may not live without managing these risks.

Among
the
different
banking
risks,
credit
risk
has
a
potential
“social”
impact
because of the number and diversity of stakeholders affected. Business failures affect
shareholders, managers, lenders (banks), suppliers, clients,
the financial community,
government,
competitors,
and
regulatory
bodies,
among
others.
In
the
age
of
telecommunications, the ripple effect of a bank failure is virtually instantaneous and
such
ripples
hold
the
potential
of
global
impact.
In
order
to
effectively
manage
the
credit risk exposure of a modern bank, there is
thus
a strong need for sophisticated
decision support systems backed by analytical tools to measure, monitor, manage, and
control, financial and operational risks and inefficiencies.

Conscious
risk- taking
decisions
call
for
quantitative
risk-management
systems,
which,
in
turn,
provide
the
bank
early
warnings
for
predicting
potential
business
failures.
Thus,
an
effective
risk-
monitoring
unit
supports
managers’
judgments
and,
hence,
the
profitability
of
the
bank.
A
potential
client's
credit
risk
level
is
often
evaluated
by
the
bank's
internal
credit
scoring
models.
Such
models
offer
banks
a
means
for
evaluating
the
risk
of
their
credit
portfolio,
in
a
timely
manner,
by
centralizing
global- exposures
data
and
by
analyzing
marginal
as
well
as
absolute
contributions
to
risk
components.
These
models
can
offer
useful
insight
and
do
provide
an
important
body
of
information
to
help
a
bank
formulate
its
risk
management
strategies.
Models
that
are
conceptually
sound,
empirically
validated,
backed
by
good
historical
data,
understood
and
implemented
by
management,
augment the business success of credit quality.

Over
the
past
decade,
several
financial
crises
observed
in
some
emerging
markets
enjoying
a
recent
financial
liberalization
experience,
showed
that
debt
financing
built
on
capital
inflow
may
result
in
large
and
sudden
capital
outflows,
thereby causing a domestic “credit crunch”. Experience with these recent crises forced
banking authorities, i.e. the Bank of International Settlements (BIS), the World Bank,
the IMF, as well as the Federal Reserve. to draw a number of lessons. Hence, they all
encourage commercial banks to
develop
internal
models to
better quantify financial
risks. The Basel Committee on Banking Supervision, English and Nelson, the Federal
Reserve System Task Force on Internal Credit Risk and Saidenberg and
Treacy and Carey represent some recent documents addressing these issues.

Credit scoring has both financial and non-financial aspects. The scope of the current
paper, however, is limited to the evaluation of a bank client's financial performance.
Studies attempting to measure firm performance on the basis of qualitative data are
exemplified by Bertels et al.

Formal or mathematical modeling of finance theory began in the late 1950s. The
work of Markowitz represents a major milestone. The practice reached its “take
-
off”
stage as a sub-discipline of Finance during the early 1960s. Some of the early efforts
were
directed
at
evaluating
a
firm
for
purposes
of
mergers
and
acquisitions;
some
dealt
with
using
investment
portfolios
to
manage
risk;
others
dealt
with
improvement/optimization
of
a
firm's
financing
mix.
They
were
all
directed
at
enhancing extant finance theory toward the goal of guiding decision-makers.

One of the
fields
in
which formal or mathematical
modeling of finance theory
has found widespread application is risk measurement. A firm's financial information
plays a vital role in decision making of risk-taking activities by different parties in the
economy.
An
extensive
literature
dedicated
to
the
prediction
of
business
failure
as
well as credit scoring concepts has emerged in recent
years. Financial ratios are the
simplest tools for evaluating and predicting the financial performance of firms. They
have been used in the literature for many decades.

The benefits and limitations of financial ratio analysis are addressed in a widely
used text on managerial finance. Financial statements report both on a firm's position
at a point in time and on its operations over some past period. However, there are still
some limitations in using ratio analysis: (i) many large firms operate in a number of
different industries. In such cases it is difficult to develop a meaningful set of industry
averages for comparative purposes; (ii) inflation badly distorts a firm's balance sheet.
Moreover, recorded values are often substantially different from their “true” values;
(iii)
seasonal
factors
can
distort
a
ratio
analysis;
(iv)
firms
can
emplo
y
“window
dressing techniques” to make their financial statements look stronger; (v) it is difficult
to generalize about whether a particular ratio is “good” or “bad”; and (vi) a firm may
have some ratios looking “good” and others looking “bad” making it d
ifficult to tell
whether the firm is, on balance, strong or weak.

Across
different
countries,
sectors
and/or
periods
of
time,
financial
ratios
that
have been found useful in predicting failure differ from study to study.

To deal with the above shortcomings of unidimensional financial ratio analysis, a
variety of methods have appeared in the literature for modeling the business failure
prediction
process.
An
excellent
comprehensive
literature
survey
can
be
found
in
Dimitras et al..

In
the
late
1960s,
discriminant
analysis
(DA)
was
introduced
to
create
a
composite
empirical
indicator
of
financial
ratios.
Using
financial
ratios,
Beaver
developed
an
indicator
that
best
differentiated
between
failed
and
non-failed
firms
using
univariate
analysis
techniques.
Altman
established
that
ratios
found
not
to
be
very significant by univariate models, could prove somewhat useful in a discriminant
function
which
considers
the
relationships
among
variables.
Hence,
he
considered
several
variables
simultaneously
using
multiple
discriminant
analysis
(MDA).
He
argued
that
MDA
had
the
advantage
of
considering
an
entire
profile
of
interrelated
characteristics common to the relevant firms. That study also aimed to predict future
failure on the basis of financial ratios. He concluded that his
bankruptcy prediction
model was an accurate forecaster of failure for up to 2 years prior to bankruptcy and
that the model's accuracy diminishes substantially as the lead-time increases. In spite
of
widespread
use
of
MDA,
Altman,
confesses
to
the
following
weakness
of
discriminant analysis:

Up to this point the sample firms were chosen either by their bankruptcy status
(Group 1) or by their similarity to Group 1 in all aspects except their economic well
being.
But
what
of
the
many
firms
which
suffer
temporary
profitability
difficulties,
but in actuality do not become bankrupt.
During
the
years
that
followed,
many
researchers
attempted
to
increase
the
success of MDA in predicting business failure. Among these are Eisenbeis; Peel et al.;
and
Falbo.
Such
work
also
involved
Turkish
firms.
Examples
are
Unal,
and
Ganamukkala and Karan.

Linear
probability
and
multivariate
conditional
probability
models
(Logit
and
Probit) were introduced to the business failure prediction literature in late 1970s. The
contribution of these methods was in estimating the probability of a firm's failure. The
linear probability model is a special case of ordinary least-squares regression with a
dichotomous dependent variable.

In the 1980s, studies utilizing the recursive partitioning algorithm (RPA) based
on a binary classification tree rationale were applied to this problem by Frydman et al.
and Srinivasan and Kim.

In the 1980s and 1990s, the use of several mathematical programming techniques
enriched
the
literature.
The
basic
goals
of
these
methods
were
to
escape
the
assumptions
and
restrictions
of
previous
techniques
and
to
improve
classification
accuracy.

In
the
early
1990s,
decision
support
systems
(DSS)
in
conjunction
with
the
paradigm
of
multi-criteria
decision-making
(MCDM),
were
introduced
to
financial
classification
problems.
Zopounidis,
Mareschal
and
Brans

Zopounidis
et
al.
Diakoulaki et al., Siskos et al. and Zopounidis and Doumpos were among the studies
that measured firm performance aiming at predicting business failure by making use
of DSS and MCDM. The ELECTRE method of Roy and the Rough Sets Method of
Dimitras et al. represent studies addressing these issues. Development and application
of
artificial
intelligence
resulted
in
the
use
of
expert
systems.
Neural
Network
methods were applied to the bankruptcy problem as well.

In
the
late
1990s,
data
envelopment
analysis
(DEA)
was
introduced
to
the
analysis
of
credit
scoring
as
in
Troutt
et
al.,
Simak,
and
Cielen
and
Vanhoof.
As
opposed to the broadly known MDA approach for business failure prediction (which
requires extra a priori information, i.e. good/bad classification), DEA requires solely
ex-post information, i.e. the observed set of inputs and outputs, to calculate the credit
scores. Thus, it opened new horizons for credit scoring.

DEA, widely known as a non-parametric approach, is basically a mathematical
programming
technique
developed
by
Charnes,
Cooper
and
Rhodes
(CCR)
to
evaluate the relative efficiency of “decision making units”
(DMUs). DEA converts a
multiplicity
of
input
and
output
measures
into
a
unit-free
single
performance
index

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