Banks play very useful and dynamic role in the economic life
of every modern state. They are an important constituent of the financial
market. They have control over a considerable part of the stock of money and
their lending and investing activities cause changes in the quantity of money
in circulation which in turn influences the nature and character of production
in any country. In fact, a country’s economic progress is judged and determined
by the progress of its banking system. To quote Bhaba (1956), “banking is the
kingpin of the chariot of economic progress. As such its role in expanding
economy of a country like India can neither be underestimated nor overlooked.”
The winds of freedom and change sweeping across the
financial sector due to reforms have thrown open several challenges to the
banks and financial institutions. The major challenges relating to introduction
of prudential norms, technological up-gradation and transparency, improving
operational efficiency in the deregulated interest rates, reduction of
non-performing assets (NPAs) for survival in the competitive market and product
innovation. (Reddy et. al., 2002; Ghosh, 1995). One of the major causes of low
profitability is the high presence of NPAs in Indian banking. Going by the
number of committees, expressions of concern, papers, programmes, etc. perhaps
the issue of NPA has become problem number one continuously for about a decade
in the banking industry. (Chhimpa, 2002).
One of the major hurdles Indian banking is facing today, is
its ever-growing size of NPAs over which the top management of almost each bank
is baffled. On account of the intricacies involved in handling the NPAs, the
ticklish task of assets management of the bank has become a tight rope-walk
affair for the controlling heads, because a little wavering ‘this or that side
‘may land the concerned bank in trouble (Banmali, 2001). Gujral (2003)
described NPAs problem as one of the greatest and the most formidable problems
that has shaken the entire banking industry in India like an earthquake.
Narsimham Committee report of 1998 has also pointed out that NPAs of the banks
constitute a real economic cost to the nation as the scarce capital is locked
up in unproductive use and consequently adversely affects the recycling of funds
with the banks.. The spiraling and the devastating effect of NPAs on the
economy has made this problem an issue of public debate and of national
priority as well. Therefore, any measure or reform on this front would be
inadequate and incomprehensive, if it fails to make a dent in NPAs reduction
and stall their growth in future, as well (Gujral,2003).
Managing NPAs is perhaps the greatest task ahead of Indian
bankers as well as planners. Over the years many strategies have been chalked
out to control its menace, but resulted in limited success. Although total
elimination on NPAs is not possible in banking business owing to externalities
but their incidence can be minimized. It is always wise to follow the proper
policy for appraisal, supervision and follow-up of advances to avoid
non-performing assets (Reddy et.al.,1999 and Naik,2001; Muniappan ,2002;.
Sood,2001; Kulkarni,1999; Singh,2002; Rao, 2002; Bose, 2005) .To control NPA
menace, preventive measures would be necessary. One of the preventive measures
is credit appraisal and credit audit. Credit appraisal usually suffers from
failures to : a) assess promoters ability to adapt to change, understand industry
and market and raise adequate
margins, b) forecast sale, and c) monitor end use of funds, cash flow etc.
Documentation of credit policy, credit audit immediately after sanction and
human resources development through training interventions are some of the
measures necessary to upgrade quality of credit appraisal in banks
(Taori,2000).Therefore, the key to handle the huge volume of NPA lies with the
efficient decision making at the branch as well at the corporate level. In this context the implementation of
adequate Information System is highly essential and desirable as banking
industry in India is on the accelerated path of computerization (Godse,
2002).The focus in this book is on the
development of Information Technology driven decision system to manage the new
loan assets, which is to be sanctioned by the banks. Configured ANN
Models(Artfiical Neural Networks) tryto overcome these problems and provide support to
decision makers in Banking field.
The methodology is based on the results of the extensive
survey of financial analysts, from the academic and banking field, as well as
on the international and national literature concerning Non–Performing Assets.
The aim is initially to identify and select the criteria (financial ratios and
qualitative criteria) that are the most appropriate to use in the evaluation of
NPA, in a second stage, to achieve the modeling of the selected criteria and
its estimation, and in a third stage representation of knowledge based on
modeling. Finally there is development of neural network followed by
validation and refinement of the
network .
It is found that majority of the factors identified through
the primary survey match the factors collected from the secondary sources.
However, some new factors like borrowers dealing with the bank, experience in
the same line of business, repayment period, no of installments, no of years of
experience in business management, track record of repayment of loan , net sale
to total loan ratio, general administrative expenditure to total expenditure
ratio etc. are also surfaced during the primary survey.
A technique of numerical representation of knowledge is
presented by modeling the selected criteria variables. The test data (i.e.
Applicant Data) is given a number representation using the modeling criteria
and estimation process. Two neural networks have been trained to classify this
data. This results into a technique for solving classification problems. A
classification procedure applied to twenty-six criteria variables is also
developed. It is found that the
Perceptron network only isolates NPA and PA cases. As a refinement the Probabilistic
Neural network is used to isolate the doubtful cases as well. Whereas in the
Perceptron network the error percentage is found to be high this gets reduced
considerably in the PNN.
The artificially generated random data set is used in the
absence of realistic training data set. It is felt that the performance of the
neural network will be significantly improved if realistic data sets are used
for training the network. In this case network will approximate an expert. A
practical limitation in training the network is that one could not use huge
training data sets (this could be an inherent limitation of MATLAB or system
resources).It may be possible that if
we train the network in a batch mode the error level may significantly dropped.
It is possible that in future the credit granting process
may become more complex and other parameters will be found to be more important
in establishing the credit worthiness of the loan applicant. In that case the
number of criteria variables may significantly increase .The classification
process i.e. the finding the NPA and PA applicants will remain the same.
However, other fields may be incorporated by modifying the system design and
software by following the outline given in the work. For creating the database
Ms-Access is used where database security is minimum as it is a DBMS. Whereas
the RDBMS such as Oracle, SQL Server has
more data security features. In realistic situations we recommend that these
databases should be used so that Applicant confidentiality is maintained.
However, with certain minor changes this program which is written in VB6.0 will
work with any RDBMS.
It is suggested that both network should be run as it is not
possible to say a priori whether a case is doubtful or not. It is possible that
by increasing the layers of the perceptron accuracy of the network can be
increased. However, we have not examined this possibility because increasing
the number of layers will require higher processing speed cpu and more memory
which may not available in all banks. Further the time to train/setup the
network will be significantly increased. Using realistic training data sets can
also reduce the error level. Further; this technique can be generalized to
solve other classification problems such as investment decisions, risk
management, financial analysis, etc.
Author(s) Details:
Dr. Bimal Deb Nath,
Assistant Professor, Department of Management, North-Eastern Hill
University, Tura Campus, Meghalaya, India.
Please see the link here: https://stm.bookpi.org/EBSLANNPNPA/article/view/14128
