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| Effective Credit Risk Management as a Tool for Sustainable Financial Performance in Banks: A Myth or Reality? | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Paper Id :
17994 Submission Date :
2023-08-14 Acceptance Date :
2023-08-22 Publication Date :
2023-08-25
This is an open-access research paper/article distributed under the terms of the Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. DOI:10.5281/zenodo.8335431 For verification of this paper, please visit on
http://www.socialresearchfoundation.com/remarking.php#8
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| Abstract |
Credit risk arises when a borrower fails to make loan repayments on schedule or does not comply with any contractual commitments, creating the possibility of incurring a loss. The most fundamental risk inherent to the bank's business model is credit risk. Financial institutions must manage the risk cautiously and comprehensively in order to survive in the world of high level of uncertainty. In accordance with an organization's risk principles, risk policies, risk process, and risk appetite as a continuous feature, the credit risk architecture offers the wide-ranging canvas and infrastructure to effectively identify, assess, manage, and control credit risk - both at the portfolio and individual levels. It has long been believed that the effective and efficient management of credit risk is the vital component of a broad-ranging approach to overall risk management and is fundamental to the safety and soundness of financial institutions. In the light of this thought, this research paper will try to evaluate the effectiveness of Credit Risk Management (CRM) practices of selected Indian public sector and private sector banks as a tool for improving financial performance of Indian commercial banks. For this purpose, return on equity (ROE), return on assets (ROA), capital adequacy ratio (CAR) and net non-performing asset ratio (NPAs) has been taken into consideration to measure the efficiency of banks. The ROE and ROA are financial performance indicators whereas CAR and net NPA ratio are credit risk management indicators.
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| Keywords | Credit Risk Management (CRM), Capital Adequacy Ratio (CAR), Return on Assets (ROA), Return on Equity (ROE), Non-Performing Assets(NPA). | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Introduction | One of the issues facing the banking industry is credit risk
management. Banks make money by providing loans to customers or businesses
depending on numerous parameters. In light of the fact that lending is a major
source of revenue for banks, when a borrower fails to pay off the loan, the
bank is at significant risk. The ‘probability of loss from a credit
transaction’ is the vanilla definition of Credit Risk. According to the BASEL
Committee, “Credit risk is most simply defined as the potential that a borrower
or counter-party will fail to meet its obligation in accordance with agreed
terms”. In accordance with an organization's risk principles, risk policies,
risk process, and risk appetite as a continuous feature, the credit risk
architecture offers the wide-ranging canvas and infrastructure to effectively
identify, assess, manage, and control credit risk - both at the portfolio and
individual levels. While preserving consistency and transparency, credit risk
management seeks to enhance and improve the effectiveness of the banks and to
protect lenders against the threat of losing money that has been credited to
them. The banking sector tends to place a high priority on streamlining credit
sanctioning, systematic risk management, and providing inputs for a bank-wide
credit policy and set of processes. The need for more sophisticated and
adaptable tools for risk management through measuring, monitoring, and limiting
risk exposures is felt as banks enter a new, high-powered world of financial
operations and trading, which comes with new dangers. Therefore, it becomes
imperative that banks' management must be adequately prepared to handle the
task of designing tools and systems that are capable of assessing, monitoring,
and scientifically controlling the credit risk exposures. A holistic approach
to overall risk management must include the effective and efficient management
of credit risk as it is essential to the safety and soundness of financial
institutions. Appropriate policies, procedures and systems should be
implemented at each financial institution to effectively identify measure,
monitor and control credit risk. On the grounds of above discussion, it has
been figured out that Credit Risk Management is a matter of contention for all
the financial institutions. In contempt of the fact that the effective credit
risk management is a significant element of intensive risk management and is
crucial for the success of any financial institution in long run, it continues
to be a challenge for all financial institutions. I, therefore, have chosen
this topic to pay close attention to analyze the effectiveness of credit risk
management practices as a tool for sound financial performance and sustainable
growth of banks and to fully understand how the risks in all their business
lines intersect and combine to affect the risk profile of the selected banks. |
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| Objective of study | The primary objectives of the study are: 1. To assess and compare the financial results of selected
Indian private sector banks and public sector banks. 2. To investigate the variables that influence the credit
risk management practices in selected Indian commercial banks, both public and
private. 3. To observe the effects of Credit Risk Management on the
financial standing of the selected Indian commercial banks. |
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| Review of Literature | Kaur, Ramanjeet (2021) in her research work “An Empirical Study of Credit Risk Management
Practices of Commercial Banks in India”stated that the banks concentrate
on the extent to which they expose themselves to danger and develop policies to
deal efficiently with it (Cummings and Durrani, 2016). The implementation of
Basel II standards and the successful implementation by RBI, in the framework
of risk management procedures, are an important step which aims to encourage
robust risk management methods. Therefore, it is very crucial at this juncture
to reduce and contain NPAs significantly within a sensible threshold (Kopra,
2017). Asima Siddique, Muhammad Asif Khan, Zeeshan Khan
(2021) in their work “The Effect of Credit Risk Management and Bank-Specific
Factors on the Financial Performance of the South Asian Commercial Banks”
indicated that NPLs, CER and LR are negatively related to FP (ROA and ROE),
while CAR and ALR are positively related to the FP of the Asian commercial
banks. The current study results recommended that policymakers of Asian
countries should create a strong financial environment by implementing the
monetary policy which stimulates interest rates in such a way that
automatically helps to lower down the high ratio of NPLs (tied monitoring
system). Nikhilkumar Shah, Nisarg Shah (2020) in his work “A
Credit Risk Management in Public and Private Sector Banks” contemplates that
Credit risk incurve due to borrower's failure to repay a loan or meet
contractual obligations which creates possibilities of loss. Nonpayment of
these loans and advances leads to bank crisis and perform as economic
development blockage. These kinds of situation have been faced in past by world
economies in term of sub-prime crisis. Thus, credit risk management in banks is
important to maintain credit risk exposure within proper and acceptable
parameters. Sundarka, Baibhav P. (2020) the main findings of the study “Empirical
Study of Credit Risk Management of Commercial Banks” is Credit Risk
Management is becoming buzz word in today’s ever dynamic business world. Modern
organized Banking Firms are concentrating more on this area as an efficient
strategy to gain competitive advantage. Paradigm shift in the field if banking
sector has made Credit Risk Management a new frontier to gain competitive advantage.
Traditional Banking practices of attracting customer through Customer
Assessment and advertisements have become outdated in nature. Nagar, Udhister (2019) “Credit risk management in commercial banks A
critical study of selected public and private sector commercial bank in India”
concluded that the bottom line for today's banking institutions,
particularly the largest and most complex ones, is that they must continue to
monitor very carefully the embedded risks of their credit products and services,
pay close attention to subtle changes in business practices that could affect
the risks related to a given product, and fully understand how the risks in all
their business lines intersect and combine to affect the risk profile of the
consolidated entity. Dalvi Madhukar &Shelankar Mitali (2018) has measured the “Impact
of Credit Risk Management on the Financial Performance of selected Public and
Private Sector Banks in India”. Data for study obtained by taking average of
five years figures of financial performance indicator namely Net Profit Margin
(NPM) and credit risk management indicators viz. Capital Adequacy Ratio (CAR),
Credit Deposit Ratio (CDR). Data has been analyzed by using Regression Model.
He concluded that Public sector banks have low credit risk and negative
profitability whereas private sector banks have high credit risk and high
profitability. Research gap The findings of the study would
be beneficial for banks' management, investors and other stakeholders
from a practical standpoint. By minimizing credit risk, bank management can
focus more on enhancing banks' performance. In light of their credit risk,
banks can thus better manage their resources. Thus, present study is a
significant endeavor forstudying, measuring, critical thinking and reviewing
the effectiveness of efficient credit risk management practices and its
implications on the overall financial performance and sustainable growth of the
bank. On the basis of the research
gap the following objectives and relevant hypothesis has been formulated for
the purpose of the study. |
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| Methodology |
Problem Statement
Effective Credit Risk Management as a Tool for Sustainable Financial Performance in Banks: A Myth or Reality?
Research Design
The study is purely based upon empirical research design. Empirical research relies on verified data to generate findings. In other words, the evidence used in this kind of research is purely based on data collected and analyzed using scientific methods.
Scope of the Study
The research work is particularly confined to three public sector banks and three private sector banks for a period of 11 years commencing from financial year 2011-12 to 2021-22. |
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| Sampling |
The sample size
is 06 Banks. The sampling technique used is probabilistic sampling technique
more specifically the random sampling. Bank Profile
Source: Compiled from Annual Reports of Selected Banks. Sources of Data
Collection
The study radically relies only on Secondary sources
of information viz; annual reports, RBI Bulletin, published journals, websites,
etc. |
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| Tools Used | The tools used for analysing data are MS-Excel and SPSS. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Statistics Used in the Study | The
collected data have been examined using several statistical methods, including
mean, standard deviation, etc. The linear regression technique has been applied
to examine the relationship and determine the impact of the independent
variables on the dependent variables. The Independent Sample t-test and the
ANOVA have also been used to test the research hypothesis and to validate the
findings. |
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| Analysis | The key variables for the proThe key variables for the problem under consideration are: Return on Assets (ROA), Return on Equity (ROE), Capital Adequacy Ratio (CAR), Net Non- Performing Assets Ratio (Net NPA), Capital Adequacy Ratio to Net Non- Performing Assets Ratio (CAR/NPA). 1. ROE: The return on equity ratio (ROE), is a financial metric which
measures a bank’s efficiency in managing the capital that shareholders have put
in it, It is a gauge of banks’
profitability and how efficiently it generates those profits. 2. ROA: A bank’s profitability in
relation to its total assets is determined by its return on assets, or
ROA Management, analysts, and investors can assess a bank’s ROA to see if
its resources are being used profitably. 3. CAR: The capital adequacy ratio
gauges a bank’s capital in relation to its current liabilities and
risk-weighted assets. According to RBI standards, scheduled commercial banks
should maintain a CAR of 9%, whilst Indian public sector banks are advised to
maintain a CAR of 12%. 4. NPA: Banks categorize loans and
advances as NPAs if the principal or interest thereon has not been paid for 90
days or more. In terms of the Net NPA to Advance ratio, the lower the ratio,
the better it is, as a lower net NPA ratio is an indication of a faster
recovery rate. An
in-depth analysis of all these key variables in respect of selected public
sector and private sector banks has been done with the help of requisite tables
and charts: Analysis of financial
performance of Indian banks Table 1.1 : Key financial indicators of Indian public sector
banks
Source:
All the figures are computed with the help of SPSS20 version The
data in table 6.1.1 depicts the key financial ratios of selected banks for the
period under study. The ROE
of SBI and BOB stands out at 8.66% and 4.63% respectively signifying effective
capital management and generation of profits.The ROA of sample banks came out
to be 0.49%, 0.30% and 0.05% for SBI, BOB and PNB respectively. The average CAR maintained by SBI, BOB and
PNB is 13.31%, 13.65% and 12.24% respectively which is considered to be safe
and thus, the public sector banks are in a position to meet its financial
obligations. It is evident from the table that the Net NPA ratio of PNB is
5.57% indicating higher NPAs. On the other hand, the Net NPA ratio of SBI and
BOB stands out at 2.69% and 2.89% respectively. The above discussion can be made clear by checking out the Chart no.1.1
below.
Source
: Graphical Representation of data in Table 6.1.1 The key variables (ROE, ROA, CAR,NPA and CAR/NPA) of the
selected public sector banks for the period have been illustrated with the help
of different colored bars. The financial indicators of SBI, BOB and PNB have
been depicted by Blue, Red and Green colored bars respectively. It is evident
from the chart that SBI has highest ROE followed by BOB. On the contrary the
ROE of PNB is negative. The SBI is earning highest return on assets followed by
BOB and PNB. The CAR maintained by BOB is higher than that of SBI and PNB.
Likewise, Net NPA of SBI and BOB is quite low as compared to that of PNB. The
CAR/NPA ratio of BOB is highest followed by SBI and PNB. Table 1.2 : Key Financial Indicators of Indian Private
Sector Banks
Source:
All the figures are computed with the help of SPSS20 version
The
data in table 6.1.2 depicts the key financial ratios of selected private sector
banks for the period under study. It is evident from the table that all the
banks are earning a highreturns on equity but the ROE of HDFC bank is highest i.e
18.24% followed by AXIS and ICICI banks thereby managing the capital
efficiently. Similarly, ROA of the banks stands out at 1.35%, 1.94% and 1.10%
for ICICI, HDFC and AXIS bank. It is noticeable here that ROA of HDFCbank is
higher than that of ICICI and AXIS bank. The average CAR maintained by ICICI, HDFC and AXIS banks are
17.78%, 16.76% and 16.33% respectively. A high CAR has been maintained
by the banks all throughout ensuring that the banks have sufficient capital
which acts like a financial cushion against losses. Among all the sample banks
the Net NPA ratio of HDFC bank is lowest i.e 0.31% followed by AXIS and ICICI
bank which stands out at 1.25% and 1.93% respectively. The data in Table 1.2
has been graphically plotted in Chart 1.2.
Source
: Graphical Representation of data in Table 1.2 The key variables (ROE, ROA, CAR, NPA and CAR/NPA) of the
selected private sector banks for the period have been illustrated with the
help of different colored bars. The financial indicators of ICICI, HDFC and
AXIS banks have been depicted by Blue, Yellow and Brown colored bars
respectively. It is significant from the chart that HDFC bank has highest ROE (depicted
by yellow color) followed by AXIS bank and ICICI bank (depicted by brown and
blue color respectively). Likewise, the ROA of HDFC bank is significantly more
than the return on assets earned by ICICI and AXIS bank which is 1.94%, 1.35%
and 1.10% respectively. There is no significant difference in the CAR of the private
sample banks. However, the yellow color of the bars signifies that HDFC bank
has minimum NPA ratio i.e 0.31% and maximum CAR/NPA ratio which is 58.22% . 2.2 Analysis of impact
of credit risk management on ROE of Indian banks The impact of credit risk
management on ROE of Indian banks for the period under study has been examined
with the help of the following equation: ROE = β0 + β1 x CAR1 + β2 x NPA1 + β3 x
CAR/NPA1 + e1 Where, Table 2.1 : Descriptive statistics
of dependent and independent variables
Source : All the
figures are computed with the help of SPSS20 version The descriptive
statistics of variables such as ROE, CAR, NPA, and CAR/NPA calculated from the
dataset of 06 private and public sector banks in India for a period of 11 years
beginning from 2012 are shown in Table 2.1. The number of observations for each
variable is 264. The mean value of dependent variable (ROE) is 9.03, whereas
the mean scoresof independent variables i.e., CAR, NPA and CAR/NPA are
15.01, 2.44and 19.02respectively. Table 2.2 :
Correlation coefficient among variables
Source : All the
figures are computed with the help of SPSS20 version Note: *Significant at 5
percent level Table 2.2 displays the
correlation coefficient between the dependent and independent variables. The
dependent variable (ROE) is correlated with CAR, NPA, and CAR/NPA, having
correlation coefficients of 0.805, -0.963,
and 0.872, respectively. Although ROE and NPA are inversely correlated; ROE,
CAR, and CAR/NPA have a strong and favorable correlation. The results also
exhibit a moderate correlation between the independent variables. Table 2.3 : Model
Summary
Source : All the
figures are computed with the help of SPSS20 version Note: *Significant at 5
percent level Table 2.3 provides the
model summary wherein the independent variables predict the
dependent variable (ROE) to a level of 96.4 percent based on the R-square
value 0.964. The ANOVA findings show that the independent variables NPA, CAR,
and CAR/NPA have a significant impact on ROE (dependent variable). Therefore,
the null hypothesis (H0.1), i.e.,there is no significant impact of credit risk management on ROE of
Indian banks, is rejected. Table 2.4 :
Coefficientsof ROE
Source : All the
figures are computed with the help of SPSS20 version Note: *Significant at 5
percent level Table 2.4 depicts the
coefficients of ROE and collinearity statistics, where all the Tolerance values
for independent variables (CAR, NPA and CAR/NPA) are more than 0.10, which
means that the Model has not violated the assumption of Multi-collinearity
assumption. These results are also supported by the VIF (Variance Inflation
Factor) values, which are less than 10. The analysis further shows that the
coefficients of all the independent variables have been included in the model
for prediction of the dependent variable. Standardized beta values are used to
compare the contribution of each independent variable. The negative
standardized beta coefficient implies an inverse relationship between the
dependent variable and independent variables. The highest beta values i.e.0.675
and 0.306 for NPA and CAR/NPA, respectively, indicate that these independent
variables contributed maximum in explaining the dependent variable. The results
of t-test indicate that the significance values of the independent
variables (CAR and NPA) are less than 0.05, therefore it is concluded that
contribution of independent variables is statistically significant in
predicting the dependent variable. 3. Analysis of Impact
of Credit Risk Management on ROA of Indian Banks The impact of credit risk management on ROA of Indian banks for the period under study has been examined with the help of the following equation: ROA= β0 + β1 x
CAR1 + β2 x NPA1 + β3 x CAR/NPA1 + e1 Where, ROA = Return on Assets of Indian Banks for
the period of 2012-2022, CAR = Capital Adequacy Ratio for the period
of 2012-22, NPA = Non-Performing Assets ratio for the
period of 2012-22, CAR/NPA = Capital Adequacy
Ratio to Non-Performing Assets for the period of 2012-22, β0 = Intercept (Constant), β1, β2, β3,
β4 = The slope
represents the degree with which bank’s performance changes as the independent
variable changes by one unit of variable,
e1 = error component. Table
3.1 : Descriptive Statistics of Independent and Dependent Variables
Source : All the
figures are computed with the help of SPSS20 version Table 3.1 lists the
descriptive statistics of variables such as ROA, CAR, NPA and CAR/NPA
calculated from the dataset of 06 public and private sector banks in India for
a period of 11 years commencing from 2012 to 2022. The number of observations
for each variable is 264. The mean value of dependent variable (ROA) is 0.87,
whereas the mean scores of independent variables i.e., CAR, NPA and
CAR/NPA are 15.01, 2.44 and 19.02 respectively. Table 3.2 :
Correlation Coefficient among Variables
Source : All the
figures are computed with the help of SPSS20 version Note: *Significant at 5
percent level Table 3.2 depicts the
correlation coefficient between the dependent and independent variables. The
dependent variable (ROA) is related to CAR, NPA, and CAR/NPA, with correlation
values of 0.901, -0.884, and 0.888, respectively. ROA and NPA are highly
negatively correlated, whereas ROA, CAR, and CAR/NPA have a strong and
favorable association. The findings also exhibit a low degree
of correlation among independent variables. Table 3.3 : Model
Summary
Source : All the
figures are computed with the help of SPSS20 version Note: *Significant at 5
percent level Table 3.3 provides a
description of the model.The R-square value of 0.979 show that the independent
variable predicts the dependent variable (ROA) to a level of 97.9 percent. The
ANOVA findings show that the independent variables CAR, NPA, and CAR/NPA have a
significant impact on ROA (dependent variable).Therefore, the null hypothesis (H0.2)i.e.there is no significant impact of
credit risk management on ROA of Indian banks, is rejected. Table 3.4 :
Coefficientsof ROA
Source : All the
figures are computed with the help of SPSS20 version Note: *Significant at 5 percent level
Table 3.4 depicts the
coefficients of ROA and collinearity statistics, where all the Tolerance values
for independent variables (CAR, NPA and CAR/NPA) are more than 0.10, which
means that the Model has not violated the assumption of Multi-collinearity
assumption. These results are also supported by the VIF (Variance Inflation
Factor) values, which are less than 10. The analysis further shows that the
coefficients of all the independent variables have been included in the model
for prediction of the dependent variable. Standardized beta values are used to
compare the contribution of each independent variable. The negative
standardized beta coefficient implies an inverse relationship between the
dependent variable and independent variables. The highest beta values i.e.0.543
and 0.510 for CAR and CAR/NPA, respectively, indicate that these independent
variables contributed maximum in explaining the dependent variable. The results
of t-test indicate that the significance values of the independent
variables (CAR, NPA and CAR/NPA) are less than 0.05, therefore it is concluded
that contribution of independent variables is statistically significant in
predicting the dependent variable. |
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| Findings | The above analysis has been done with the help of data compiled from the various annual reports of the sample banks. In this regard, the findings of the study are as follows: 1. There is a significant impact of NPA, CAR, and CAR/NPA (independent variables) on ROE (dependent variable) of Indian banks. Therefore, the null hypothesis H0.1 is rejected and alternate hypothesis H1.1 is accepted. 2. There is a significant impact of NPA, CAR, and CAR/NPA (independent variables) on ROA (dependent variable) of Indian banks. Therefore, the null hypothesis H0.2 is rejected and alternate hypothesis H1.2 is accepted. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Conclusion |
After analyzing the data, it is apparent that the financial
performance of both the public and private sector banks is as fine as fiddle.
However, a comparative analysis of data brings to view that private banks
perform better than public sector banks because their CAR, ROA, and ROE ratios
are better than those of the latter. This would imply that private banks are
making better use of the investor's funds to create income. The return on
assets ratio reveals another noteworthy difference that the private sector
banks are producing higher returns on their assets in comparison to public
banks. The CAR for private banks is likewise higher, demonstrating their
adequate capacity for absorbing risks. Moreover, in comparison to public sector
banks, the NPA for private banks is quite low. Hence, the public sector banks
must concentrate on minimizing their NPAs and improving their CAR in order to
perform better. Thus, it can be contemplated that “Effective Credit Risk
Management as a Tool for Sustainable Financial Performance in Banks is a
reality and not a myth”. |
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| Suggestions for the future Study | 1. Banks must concentrate on boosting non-interest income. To raise revenues, the banks must look to expand into a wide range of financial services, such as factoring, mutual funds, leasing, portfolio management, and merchant banking, which will open up the fresh sources of lucrative income for banks. 2. A substantial customer base has been captivated by the private banks due to their quick and effective working methods, attractive schemes, cutting-edge technologies, and superior customer service. Therefore, in order to provide effective customer services, public sector banks must also adopt the most recent advancements. 3. One of the most vital signs of a bank's financial health is the quality of its assets. Due to reduced levels of recovery, the new deposition of NPAs has been accumulating more rapidly than the reduction of existing NPAs. Therefore, banks must adhere to the appropriate mid-term credit assessment policy, supervise and track advances, improve asset quality, and implement an efficient internal control system in order to prevent NPAs. A significant diminution in NPAs is required to boost the profitability. 4. To reduce credit risks, banks must establish strong credit administration committees and teams, as well as adequate credit rules that meet all relevant requirements before giving credit to consumers. These committees and teams must undertake suitable and sound loan appraisal evaluations, as well as supervise the loan process from the time a loan is advanced until the time it is repaid. |
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| Limitation of the Study | The key limitations of the study are as follows: 1. This research is time bound and only certain criteria have been taken up for the study. 2. All the computations have been done on the basis of data as at balance sheet date. 3. The study revolves around 3 public sector and 3 private sector commercial banks representing Indian Banking Sector. Hence, findings may differ for other banks. 4. The secondary data has its own limitations. |
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| Acknowledgement | Researcher would like to express my heartfelt gratitude to her Ph.D supervisor Prof. Arvind Kumar Sir for his invaluable guidance and support throughout the writing of the paper. His insightful feedback and constructive criticism have helped me torefine my ideas and improve the quality of her work. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| References | 1. Bagchi, S.K. (2004). Credit Risk Management (3rd
edition.). Jaico Publishing House. |
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