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Risk management
has assumed increased importance
from the regulatory compliance point
of view. Credit Risk being an important
component of risk, has been adequately
focused upon. |
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Credit risk management
can be viewed at two levels - at
the level of an individual asset
or exposure and at the portfolio
level. Credit risk management tools
therefore have to work at both individual
and portfolio levels. |
Traditional
tools of credit risk management include
loan policies, standards for presentation
of credit proposals, delegation of loan
approving powers, multi-tier credit approving
systems, prudential limits on credit exposures
to companies and groups, stipulation of
financial covenants, standards for collaterals,
limits on asset concentrations and independent
loan review mechanisms.
Monitoring
of non-performing loans has however a
focus on remedy rather than advance warning
or prevention. Banks assign internal ratings
to borrowers, which will determine the
interest spread charged over PLR. These
ratings are also used for monitoring of
loans.
Some
central banks like the Reserve Bank of
India have suggested the use of rating
models like Altman's Z score models at
individual loan/company level and risk
models like CreditMetrics and CreditRisk+
at the portfolio level.
While
evaluating credit and monitoring, banks
use a number of financial ratios. There
have been studies of predictive ability
of various ratios.
Attempts
at combining different ratios into a single
measure by using the statistical technique
of 'Multiple Discriminant Analysis' have
also been made. Among these, Altman's
Z-Score is well known.
It
forecasts the probability of a company
entering bankruptcy. The model combines
five financial ratios into a single index.
Practitioners however had difficulties
in using the model, as the classification
error is high for more than one year in
advance.
Thus
by the time the model could be applied
to published financial data, it would
be too late for any action to be taken.
Recently,
significant advances have been made in
credit risk modelling at the portfolio
level. The interest is not confined to
academicians alone. Policy makers and
practitioners are also seriously working
on applying these models.
Two
of the models, CreditMetrics and CreditRisk+,
have been released freely to the public
by their respective creators since 1997.
CreditMetrics
was developed by J P Morgan and focuses
on estimating the volatility of asset
values caused by variations in the quality
of assets. To compute volatility, the
model tracks the 'rating migration' –
the probability that a borrower of one
risk rating migrates to another risk rating.
CreditRisk+
was developed by Credit Suisse Financial
Products. It is a statistical method for
measuring and accounting for credit risk.
The model is based on actuarial calculation
of expected default rates and unexpected
losses from default.
The
model is based on insurance industry models
of event risk. Under CreditRisk+, each
individual obligor has a default probability.
The default probability is not constant
over time but changes in response to background
economic factors.
To
the extent that more than one obligors
are sensitive to the same background economic
factors, their default probabilities move
together, which can lead to correlations
in defaults.
Can
banks go ahead and adopt models in their
Credit Risk Management process? Which
model to go for?
Direct
comparison of models is not simple, as
different models may be presented with
rather different mathematical frameworks.
For example, given the same portfolio
of credit exposures, the two models mentioned
above have been found to be, in general,
yielding differing evaluations of credit
risk.
Actually,
the problem is not just that of selection
of a model but that of validating the
model chosen. As credit risk models employ
relatively longer time horizons (one year
to several years), their validation poses
a major difficulty in requiring many years
of historical data spanning multiple credit
cycles for estimating key parameters accurately.
As
a contrast, market risk models use a much
shorter time horizon and their 'backtesting'
becomes simpler. Practitioners and researchers
alike have reported 'data insufficiency'
to be a key impediment to the design and
implementation.
In
this context, it will be useful to note
that the Basel Committee has comprehensively
looked at the use of credit risk models
and made some interesting observations.
The Task Force of the Basel Committee
recognizes that credit risk modeling may
result in better internal risk management.
However,
it is critical that regulators are confident
that models are conceptually sound and
empirically validated before they can
be used in the process of supervisory
process and computation of capital requirements.
The
task force has rightly recognized that
data availability and model validation
are two hurdles to be crossed before the
next step is taken. In fact, the recent
revisions to the 1988 Basel Accord, do
not envisage permitting banks to set their
capital requirements solely on the basis
of their own credit risk models.
Internationally,
the degree to which models have been incorporated
into the credit management and economic
capital allocation process varies greatly
between banks.
Large
sized banks across the world have put
in place risk adjusted return on capital
framework for pricing of loans. Banks
have implemented different models for
corporate and retail businesses.
While
only a small number of banks are currently
using models for active credit risk management,
the internal applications are varied and
include setting of concentration and exposure
limits, risk-based pricing, evaluation
of risk-adjusted performance of business
lines or managers and customer profitability
analysis.
As
discussed above, credit risk models require,
most importantly, historical loan loss
data and other model variables, spanning
multiple credit cycles.
Banks
must therefore, as a first step, endeavor
building adequate a database for implementing
credit risk modeling over a period of
time.
Even
more important and urgent is the need
to take a hard look at the borrower rating
systems currently used by the banks. Banks
have not done even basic attempts to test
and revise these systems using historical
data on defaults.
One
of the first things to do therefore is
to rework the borrower rating systems
and make them really reflective of risk.
Separate rating frameworks may be necessary
for different customer segments.
Most
banks will need expert help in the preparatory
and implementation phases – education
and training, study of available models,
building models depending on a bank's
business profile, model validation, data
sufficiency studies and building systems
for ongoing data build up.
To
be able to move swiftly in this area,
banks need to work from the sides of both
the business analytics and the supporting
technology infrastructure.
It
is going to be some significant investment,
but considering that it is 'risk management'
that they are going to spend on, it should
be worthwhile!
The author, H S Rajashekhar, is with i-flex
solutions, India. The views expressed
in this article are his own. He can be
contacted on e-mail: hs.rajshekhar@iflexsolutions.com
Mr.
Rajashekhar is a senior banker
with over 19 years of aggregate
experience in Banking, Financial
Services and Consulting.
Before joining i-flex consulting,
Raj has held senior positions
in banking with responsibilities
both in Corporate and Retail Banking.
His exposure includes Credit Management,
Leasing, Hire Purchase, Foreign
Exchange and Strategic Management
of Banks.
With
i-flex consulting, Raj has worked
on assignments in the U.S., Japan,
Europe and Africa. He is currently
with the Risk Management Practice
of i-flex consulting and is involved
in projects for implementation
of Basel II.
Raj
earned a Masters Degree in Commerce
from Mumbai University and a PG
Diploma in Bank Management from
the National Institute of Bank
Management, Pune (India). He is
a qualified Chartered Financial
Analyst from ICFAI, India. Raj
is also a Certified Associate
of the Indian Institute of Bankers.
Raj regularly writes for the financial
press in India.
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