credit risk analysis
DESCRIPTION
Credit Risk Analysis inIndian Commercial BanksTRANSCRIPT
Credit Risk Analysis in Indian Commercial Banks-An Empirical Investigation
Authors:Swaranjeet AroraAssistant Professor (SG), Prestige Institute of Management and Research, Indore, India. Email: [email protected]
AbstractRisk exposure in banking system has increased due to fierce competition, changing socio-
economic patterns, market flexibility, and increased foreign exchange business and cross
border activities. These developments have resulted into various types of banking risks.
Credit risk, earlier present in the banking system has also increased and Credit risk analysis
has emerged as a big challenge for the Indian commercial banks. This paper attempts to
identify the factors that contribute to Credit Risk analysis in Indian banks and to compare
Credit Risk analysis practices followed by Indian public and private sector banks, the
empirical study has been conducted and views of employees of various banks have been
tested using statistical tools. Present study explored the phenomenon from different
perspectives and revealed that Credit Worthiness analysis and Collateral requirements are
the two important factors for analyzing Credit Risk. From the descriptive and analytical
results, it can be concluded that Indian banks efficiently manage credit risk. The results also
indicate that there is significant difference between the Indian Public and Private sector
banks in Analyzing Credit Risk.
Keywords: Risk management; Banks; Credit Risk
Introduction
“Granting credit involves - accepting risk as well as
producing profits”
-Bank for international settlements, Basel, Switzerland
There has been tremendous transition in the role of bank as
a financial intermediary. Before liberalization all the
activities of banks were regulated and hence operational
environment was not conducive to risk taking. Now, banks
have grown from being a financial intermediary into a risk
intermediary. Banks are exposed to severe competition and
hence are compelled to encounter various types of financial
and non-financial risks. Risks and uncertainties form an
integral part of banking which by nature entails taking
risks. Banks are now required to clearly discriminate
avoidable and unavoidable risks and are required to focus
on the extent to which such risks can be taken by banks.
The banking reforms and policy changes during the years
have gradually changed banking landscape and credit
market in India. First visible change is that banks are now
more customer focused and are providing innovative
products at fast pace, Second change is that deregulation
has made the banks free to formulate their own schemes
and products as per their market segment and risk appetite,
redesign business process and lending policies and
procedures to meet changing expectations of the customers
and the market. Thirdly, introduction of risk management
practices and implementation of Basel II recommendations
have brought in more professional approach in credit
delivery process which is now more risk focused and has
made pricing of loan-products dependent on risk
perception of the borrower and likely hood of default.
Fourth visible change is that banks are moving from so
called lazy banking to busy banking by aggressively
Asia-Pacific Finance and Accounting ReviewISSN 2278-1838: Volume 1, No. 2, Jan - Mar 2013
expanding credit to retail, agriculture and small and
medium enterprises. Fifth visible change is that banks are
gradually becoming super market where they will not only
lend but also offer whole gamut of financial products
including third party products so that customer gets
opportunity to select best product at competitive price. All
these changes are on the one hand creating new business
opportunities and on the other hand also creating new
challenges, which banks will have to face boldly and
proactively (Mehrotra, 2005).
Banking risk results into Expected and Unexpected losses.
Banks rely on their capital as a buffer to absorb such losses.
Chakrabarti and Chawla (2005) suggested that banks must
plough back profit to build profound and solid reserve
base. According to experts banks need to maintain enough
capital for prudential corrective action to prevent any risk
(Bhat 2005). The efficiency of capital plays a major role in
this exercise and banks are advised to adopt risk
management practices. Eichengreen (1999) identifies the
policies of the new international financial architecture as
crisis prevention, crisis prediction and crisis management.
In spite of heavy regulations in the last two decades, many
developed and emerging countries have witnessed severe
banking crises. There is an imperative need to follow
internationally compatible prudential norms relating to
capital structure and supervisory norms. Banks are
required to develop the system which involves minimum
risk exposure.
Credit risk in commercial banks represents the most
important type of risk. Banks bear the credit risk attached to
bank loans and forward contracts. The risk of defaults or
protracted arrears on outstanding loan is termed as credit
risk (Tamimi, H. and Mazrooei, F., 2007). According to the
consultative paper issued by the Basel Committee on
Banking Supervision (1999), for most banks loans are the
largest and most obvious sources of credit risk. Credit Risk
is the potential that a bank borrower or counter party fails
to meet the obligations on agreed terms. It mainly arises
from the potential that a borrower or counterparty will fail
to perform on an obligation. It may arise from either an
inability or an unwillingness to perform in the pre-
committed contracted manner.
Financial markets in developing economies are not sound
and efficient and are predominantly occupied by State-
owned firms. State-owned firms, especially in banking
sector, are commonly found in many developing countries
(La Porta et al. 2002). Banking Policies and Strategies are
formed depending upon type and structure of ownership
of a bank. Organizational culture, attitude and behaviors
also vary according to type of bank ownership i.e. Private-
owned banks and state owned banks. This difference leads
to different levels of risk- taking behavior and banks
performance (Arora, S. and Jain, R.; 2011) and in turn
26
results into varying level of Credit Risk in different types of
banks. This paper is aimed to examine the degree to which
Indian banks analyze Credit Risk and attempts to identify
the factors that contribute to Credit Risk Analysis in
different commercial banks and to compare whether Public
and Private Sector banks efficiently analyze Credit risk.
Various researchers have studied reasons behind bank
problems and identified several factors (Santomero, 1997;
Basel, 1999, Basel, 2004). Bindseil, U. and Papadia, F. (2006)
reviewed the role and effects of the collateral frameworks
which central banks, and in particular the Euro system, use
in conducting temporary monetary policy operations.
They explained the design of such a framework from the
perspective of risk mitigation, which is the purpose of
collateralization. They identified that by means of
appropriate risk mitigation measures, the residual risk on
any potentially eligible asset can be equalized and brought
down to the level consistent with the risk tolerance of the
central bank. Once this result has been achieved, eligibility
decisions should be based on an economic cost-benefit
analysis. They also looked at the effects of the collateral
framework on financial markets, and in particular on
spreads between eligible and ineligible assets.
Gilbert and Wilson (1998) examined the impact of banking
deregulation on the productive efficiency of Korean
private banks during the 1980 and 1994 reporting
productive efficiency improvements following the 1980s
deregulation. A banking crisis can also be initiated by a
high level of unexpected non-performing loans in a bank.
When this information is known by the depositors, they
rush to the bank to get back their deposits before the other
depositors. If markets for liquidity are inefficient because
of market power or information asymmetries, liquidity
problems at healthy banks can turn into solvency
problems. In fact, in this case the bank is forced to sell its
long-term assets below their fair value, see, e.g., Allen and
Gale (1998), Bernanke and Gertler (1989), Donaldson
(1992), Kiyotaki and Moore (1997), and Kwan and Eisenbis
(1997) demonstrate that inefficient banks are more prone to
risk-taking.
Relationship between capital, risk and efficiency varies for
banks with different ownership structures. However, there
is little empirical guidance to suggest whether there are
systematic differences in the relationship between risk
taking, capital strength and efficiency for banks with
different ownership features. Much of the literature on
banking in emerging markets focuses on either the broad
relationship between ownership and financial
performance (e.g., Sarkar, Sarkar and Bhaumik, 1998) or
the agency aspect of ownership, i.e., the impact of
separation between management and ownership on the
Literature Review
Swaranjeet Arora
performance of banks (e.g., Gorton and Schmid, 1999;
Hirshey, 1999).
Previous studies found that foreign-owned banks
outperform domestic-owned banks in developing
countries (Havrylchyk 2003). State-owned banks
underperform domestic-owned banks (Bonin et al. 2003;
Cornett, Guo, Khaksari, and Tehranian 2000). Bonin et al.
(2004) argued that over the second half of the 1990s, foreign
ownership in the banking sectors of transition countries
increased dramatically and the performance of foreign
owned banks were significantly higher than domestically
owned banks and the extent of such foreign ownership
impacted the bank efficiency significantly in eleven
transition countries.
The International Monetary Fund (2000) noted that
subsequent to privatization of banks in Bulgaria, following
the banking-currency crisis of 1996-97, the banking sector
was reluctant to lend in the high-risk environment,
resulting in a ratio of private sector credit to GDP of about
12 percent. This is compared to the optimal value of this
ratio for a country with Bulgaria’s per capita GDP of
around 30 percent. Latin American evidence suggests that
foreign banks are especially risk averse and that significant
market penetration by these banks in a developing
economy context might adversely affect credit disbursal to
small and medium enterprises (Clarke, Cull, D’Amato and
Molinari, 1999; Clarke, Cull, and Peria 2001; Clarke, Cull,
Peria and Sanchez, 2002).
Coleman, L. (2007) provided a practical explanation of the
risk taking behavior of finance executives and confirms
that context is more important to decisions than their
content. He also explored reasons for decision makers
facing choices preferring a risky alternative. He finally
identified the risk propensity and quantified it by
respondents’ attitude towards a risky decision, and also
explained decision maker traits using independent
variables. Oldfield and Santomero (1997) investigated risk
management in financial institutions. In this study, they
suggested four steps for active risk management
techniques:
1. The establishment of standards and reports;
2. The imposition of position limits and rules (i.e.
contemporary exposures, credit limits and position
concentration);
3. The creation of self investment guidelines and
strategies; and
4. The alignment of incentive contracts and
compensation (performance-based compensation
contracts).
Scope and Design of the Study
Objectives
The present investigation is based on exploratory research
inquiry and examines the Credit Risk Analysis process in
Public and Private sector banks. It is based on primary data
and compares Credit Risk Analysis process in Indian
Public and Private sector banks of Indore division. The data
was collected from sample of 200 employees of public and
private sector banks of Indore division. 50 respondents
were chosen from each bank viz SBI and Associates; Other
Nationalized Banks; Old Private Sector banks and New
Private Sector Banks. The respondents were selected
through non-probability convenience (judgmental)
sampling method.
As this research has a quantitative base so questionnaire
used in this research is close ended questionnaire. The
research instrument used to collect data was based on
questionnaire developed by Al-Tamimi and Al-Mazrooei
(2007). It included seven close-ended questions based on an
interval scale. Respondents were asked to indicate their
degree of agreement with each of the questions on a five-
point Likert scale. The data were analyzed using window
based Statistical package of the Social Science (SPSS). The
statistical tools used were analysis of variance, Tukey
(HSD) test, Kaiser- Meyer- Olkin (KMO), Bartlett’s test,
Factor Analysis and mean were used to analyze the data.
Questionnaire adopted in this study consisted of seven
questions. As the sample size was 200, item with
correlation value less than 0.1948 should be dropped. All
the items in the study had correlation values more than
0.1948 thus; no item was dropped from the questionnaire.
Reliability of the measures was assessed with the use of
Cronbach’s alpha on all the seven items. Cronbach’s alpha
allows us to measure the reliability of different variables. It
consists of estimates of how much variation in scores of
different variables is attributable to chance or random
errors (Selltiz et al., 1976). As a general rule, a coefficient
greater than or equal to 0.7 is considered acceptable and a
good indication of construct reliability (Nunnally, 1978).
The Cronbach’s alpha for the questionnaire was 0.813.
Hence, it was found reliable for further analysis.
1. To compare whether Public and Private Sector banks
analyze Credit Risk efficiently.
2. To explore the factors contributing to Credit Risk
Analysis in banks.
3. To open up new vistas of research and develop a base
for application of the findings in terms of implications
of the study.
27
Credit Risk Analysis in Indian Commercial Banks-An Empirical Investigation
Hypotheses
Results and Discussion
H : There is no correlation among seven variables in the 01
population under study.
H : There is no significant difference between SBI and 02
Associates, Other Public sector Banks, New Private
Sector Banks and Old Private sector Banks in practice
of Credit Risk Analysis.
H : There is no significant difference between SBI and 03
Associates and Other Public sector Banks in practice
of Credit Risk Analysis.
H : There is no significant difference between SBI and 04
Associates and New Private Sector Banks in practice
of Credit Risk Analysis.
H : There is no significant difference between SBI and 05
Associates and Old Private sector Banks in practice
of Credit Risk Analysis.
H : There is no significant difference between Other 06
Public sector Banks and New Private Sector Banks in
practice of Credit Risk Analysis.
H : There is no significant difference between Other 07
Public sector Banks and Old Private Sector Banks in
practice of Credit Risk Analysis.
H : There is no significant difference between Old 08
private sector banks and New Private Sector Banks
in practice of Credit Risk Analysis.
To test the correlation among all the variables in the
population under study, Kaiser- Meyer- Olkin (KMO)
measure of sampling adequacy and the Bartlett’s test of
sphericity were performed and to test the significance of
variance and understand inter-level difference between
and within group treatments, the data were treated with F-
test analysis.
Results of KMO and Bartlett’s test of sphericity
As indicated in Table-1 the generated score of KMO was
0.676, reasonably supporting the appropriateness of using
factor analysis. The Bartlett’s test of sphericity was highly
significant (p<0.01), rejecting the null hypothesis (H01) that
the seven variables are uncorrelated in the population.
Using Principal components with varimax rotation only
attributes with factor loadings of 0.5 or greater on a factor
were regarded as significant. The factor analysis generated
two factors explaining 72.28% of the variability in the
original data.
28
Results of Factor Analysis
Credit worthiness Analysis: It represents specific and
overall analysis of clients in respect of loan granted to them
in order to reduce credit risk. It is measured by items 2, 1, 3,
7 and 6 as identified in table 3. These items are “Before
granting loans your bank undertake a specific analysis
including the client’s characters, capacity, collateral capital
and conditions”, “This bank undertakes a credit
worthiness analysis before granting loans”, “This banks’
borrowers are classified according to a risk factor (risk
rating)”, “The level of credit granted to defaulted clients
must be reduced” and “It is preferable to require collateral
against some loans and not all of them” table 2 display that
Variable 2 is strongest and explains 44.00 per cent variance
and has total factor load of 0.842.
Collateral Requirement: It represents guarantee against
the loan granted so as to reduce credit risk of the bank. It is
measured by items 4 and 5 as identified in table 3. These
items are “It is essential to require sufficient collateral from
the small borrowers” and “This bank’s policy requires
collateral for all granting loans” table 1 display that
variable 4 is strongest and explain 72.28 percent variance
and has total factor load of 0.913.
Results of ANOVA
To test the significance of variance and understand inter-
level difference between and within group treatments, the
data were treated with F-test analysis (Table-4).
H stands rejected02
Credit Risk analysis in SBI and associates, Other Public
sector banks, Old Private sector banks and New Private
sector banks significantly differ in their mean values (F=
26.242 and p< 0.01). Old Private sector banks has highest
mean value of 212, hence have better Credit Risk analysis.
New Private Sector banks with mean value of 199.5, SBI
and associates with mean values of 178.5 and Other Public
sector banks with mean value of 168 represents that Credit
Risk analysis in these banks are comparatively less
effective.
To test the significance of difference between means of each
of the subgroups Tucky test was applied (Table-5)
H stands accepted03
Credit Risk analysis in SBI and associates and Other Public
sector banks do not significantly differ in their mean values
(p> 0.05); this means null hypothesis H cannot be rejected 03
at 5% significance level and it can be inferred that there is no
significant difference between Credit Risk analysis in SBI
and Associates (X =178.5) and Other Public sector Banks (X
=168).
Swaranjeet Arora
H stands rejected04
Credit Risk analysis in New Private Sector Banks and SBI
and associates significantly differ in their mean values
(p<0.05); this means null hypothesis H can be rejected at 04
5% significance level and it can be inferred that Credit Risk
analysis in New Private Sector Banks (X =199.5) is
significantly better then SBI and Associates (X =178.5).
H stands rejected05
Credit Risk analysis in Old Private sector Banks and SBI
and associates significantly differ in their mean values
(p<0.05); this means null hypothesis H can be rejected at 05
5% significance level and it can be inferred that Credit Risk
analysis in Old Private sector Banks (X =212) is significantly
better then SBI and Associates (X =178.5).
H stands rejected06
Credit Risk analysis in New Private sector Banks and Other
Public sector Banks significantly differ in their mean values
(p<0.05); this means null hypothesis H can be rejected at 06
5% significance level and it can be inferred that Credit Risk
analysis in New Private Sector Banks (X=199.5) is
significantly better than Other Public sector Banks (X=168)
H stands rejected07
Credit Risk analysis in Old Private Sector Banks and Other
Public sector Banks significantly differ in their mean values
(p<0.05); this means null hypothesis H can be rejected at 07
5% significance level and it can be inferred that Credit Risk
analysis in Old Private Sector Banks (X = 212) is
significantly better than Other Public sector Banks (X =168)
H stands accepted08
Credit Risk analysis in Old Private Sector Banks and New
Private Sector Banks do not significantly differ in their
mean values (p> 0.05); this means null hypothesis H 08
cannot be rejected at 5% significance level and it can be
inferred that there is no significant difference between
Credit Risk analysis in Old Private Sector Banks (X =212)
and New Private Sector Banks (X =199.5). significantly.
A body of literature on consumer lending has shown that
asymmetric information may prevent the efficient
allocation of lending, resulting in credit rationing (Jaffee
and Russell, 1976; Stiglitz and Weiss, 1981). According to
this literature, because of the existence of informational
asymmetries, lenders fail to observe some relevant
characteristics of potential borrowers and have no way of
learning about them. Were full information available, the
volume and distribution of lending would doubtless be
very different from the outcome under asymmetric
information (deMeza and Webb, 2000). Godlewski (2006)
investigated regulatory and institutional determinants of
credit risk taking and bank's default probability in
emerging market economies. Using a two step logit model
applied to a database of banks from emerging economies,
they confirmed the role of the institutional and regulatory
environment as a source of excess credit risk, which
increases a bank's default risk.
Salas and Saurina (2002) examined credit risk in Spanish
commercial and savings banks; they used panel data to
compare the determinants of problem loans of Spanish
commercial and savings banks in the period 1985-1997,
taking into account both macroeconomic and individual
bank-level variables. The GDP growth rate, firms, family
indebtedness, rapid past credit or branch expansion,
inefficiency, portfolio composition, size, net interest
margin, capital ratio and market power are variables that
explain credit risk. Their findings raise important bank
supervisory policy issues: the use of bank-level variables as
early warning indicators, the advantages of mergers of
banks from different regions, and the role of banking
competition and ownership in determining credit risk.
Al-Tamimi (2002) investigated the degree to which the
UAE commercial banks use risks management techniques
in dealing with different types of risk. The study found that
the UAE commercial banks were mainly facing credit risk.
The study also found that inspection by branch managers
and financial statement analysis were the main methods
used in risk identification. The main techniques used in risk
management according to this study were establishing
standards, credit score, credit worthiness analysis, risk
rating and collateral; the study also highlighted the
willingness of the UAE commercial banks to use the most
sophisticated risk management techniques, and
recommended the adoption of a conservative credit policy.
Bindseil, U. and Papadia, F. (2006) reviewed the role and
effects of the collateral frameworks which central banks,
and in particular the Euro system, use in conducting
temporary monetary policy operations. They explained the
design of such a framework from the perspective of risk
mitigation, which is the purpose of collateralization. They
identified that by means of appropriate risk mitigation
measures, the residual risk on any potentially eligible asset
can be equalized and brought down to the level consistent
with the risk tolerance of the central bank. Once this result
has been achieved, eligibility decisions should be based on
an economic cost-benefit analysis. They also looked at the
effects of the collateral framework on financial markets,
and in particular on spreads between eligible and ineligible
assets.
Powell et. al. (2004) analyzed how data in public credit
registries can be used to strengthen bank supervision and
to improve the quality of credit analysis by financial
institutions. The study was performed in central banks of
Argentina, Brazil and Mexico. The results of the empirical
29
Credit Risk Analysis in Indian Commercial Banks-An Empirical Investigation
tests explored that credit analysis enhances credit risk for
capital and provisioning requirements and acts as a check
on a bank’s internal ratings for the Basel II’s internal rating-
based approach. Arora, S.; Chatterjee, A. and Hyde, A.
(2007) analyzed credit risk management system employed
by Public and Private sector banks in India. They found that
Net NPA to Net Advances ratio, Gross NPA to Gross
Advances ratio & Credit Deposit ratio are important
parameters while evaluating Credit risk management
systems of banks. They also found that Credit Risk
management system of banks can be improved if proper
emphasis is given to these parameters.
Jayadev (2006) identified a set of actions to improve the
quality of internal rating models of Indian banks by
analyzing internal credit rating practices of Indian banks.
The survey revealed that the components of internal rating
systems, their architecture, and operation differ
substantially across banks. The range of grades and risks
associated with each grade also varied across the banks
analyzed which implied that lending decisions may vary
across banks. There were differences among the rating
systems of various banks. Arora, S. and Jain, R.(2011)
identified the factors that contribute to Risk Assessment
and Analysis in Indian banks and found that Risk
Measurement and Probability of occurrences were the two
factors for Risk Assessment and Analysis. They also
concluded that Indian banks efficiently assess and analyze
risk in general but there was significant difference between
the public and private sector banks in Risk assessment and
analysis.
Cost of delaying or avoiding proper risk management can
lead to some adverse results, like failure of a bank and
possibly failure of a banking system (Meyer, 2000). Altman
(2002) analyzed that more emphasis is given to new
developments and techniques for analytical treatment of
credit risk management. Information sharing through
credit bureaus is important for a number of reasons: it may
increase the degree of competitiveness within credit
markets, improve efficiency in the allocation of credit, and
increase the volume of lending (Vives, 1990). Credit scoring
is the process of assigning a single quantitative measure or
score to a potential borrower, representing an estimate of
the borrower's future loan performance (Feldman, 1997).
In present study an attempt has been made to cover most of
the aspects of Credit risk Analysis. However, this paper did
not address in detail credit risk practices adopted by Indian
commercial banks. This type of study can be addressed in
future studies as credit risk represents the most challenging
type of risk. Further research can also be done on Basel II
accord and Credit risk management. Finally, the study
could usefully be conducted in another country, using the
Areas for Further Research
30
same methodology. Different and interesting results may
be expected, because credit risk Analysis is affected by
specific factors such as economic conditions, competition
and regulations.
This paper examined Credit Risk analysis system in Public
and Private sector banks of India. Credit risk Analysis is
crucial because no single database typically houses all of
the risk related data and several years of information is
required. The present study has indicated that Credit
Worthiness analysis and Collateral requirements are the
two important factors for analyzing Credit Risk. From the
descriptive and analytical results, it can be concluded that
Indian banks efficiently manage credit risk. The results also
indicate that there is a significant difference between the
Indian Public and Private sector banks in Credit Risk
Analysis. Credit Risk Analysis is better in Old Private
sector banks and New Private Sector banks, as compared to
State Bank of India and its associates and other public
sector banks. This reflects that in order to improve Credit
Risk Analysis system in banks, efforts should be made to
train the employees so as to improve their understanding
of credit risk, proper credit risk identification,
measurement, monitoring and control system should be
implemented throughout the bank and in the process due
emphasis is required to be given to Credit Worthiness
analysis and Collateral requirements.
• A Roadmap for Implementing an Integrated Risk
Management System by Indian Banks by March 2005-
CRISIL (2004). IBA Bulletin special Issue, 26 (1), 37-55.
• Allen, F. and Douglass, G (1998). Optimal Financial Crises.
Journal of Finance, 53 (4), 1245-84.
• Al-Tamimi, H. (2002). Risk management practices: an
empirical analysis of the UAE commercial banks. Finance
India. 16 (3), 1045-57.
• Al-Tamimi, H. and Al-Mazrooei, F. (2007). Banks' Risk
Management: A Comparison Study of UAE National and
Foreign Banks. The Journal of Risk Finance, 8(4), 394 – 409.
• Altman, E. (2002). Managing Credit Risk: A Challenge for
the new millennium. Review of Banking, Finance and
Monetary Economics, 31(2), 201-214.
• Arora, S.; Chatterjee, A. and Hyde, A. (2007). Credit Risk
Management in Indian Banks: A Comparative Study.
Sameeksha- Technia journal of Management Studies. 1 (2).
• Arora, S. and Jain R. (2011). Exploring Risk Assessment
and Analysis practices in Indian Commercial Banks. ELK
Asia Pacific Journal of Finance and Risk Management, 2(3),
605-614.
• Bernanke, B. and Mark G (1989). Agency Costs, Net Worth
and Business Fluctuations. American Economic Review, 79
(1), 14-31.
Conclusion
References
Swaranjeet Arora
• Bhat, S (2005), “Capital Adequacy: Regulation and Bank
Evidences” Synthesis- The Journal of BLS Institute of
Management, Ghaziabad, 2(2),18-30.
• Bindseil, U. and Papadia, F. (2006). Credit Risk Mitigation
in Central Bank Operations and Its Effects on Financial
Markets: the Case of the Eurosystem. European Central
bank, occasional paper series no.49.
• BIS, Basel Committee on Banking Supervision (1999), “A
New Capital Adequacy Framework” Bank for International
settlements, Switzerland, CP1
• Bonin (2003). Privatization Matters: Bank Performance in
Transition Countries. Conference on Bank Privatization
World Bank, Washington, D.C. November 20-21.
• Bonin, J.; Hasan, I. and Wachtel, P. (2004). Bank
performance, efficiency, and ownership in transition
countries. Journal of Banking & Finance, 29(1), 31–53.
• Chakrabarti, R and Chawla, G (2005). Bank Efficiency in
India since the reforms - An Assessment. Money and
Finance, 2 (22-23), 31-48.
• Clarke, G.; Cull, R. and Maria S. (2001). Does foreign bank
penetration reduce access to credit in developing countries?
Evidence from asking borrowers, Mimeo, Development
Research Group. The World Bank, Washington, D.C.
• Clarke, G.; Cull, R.; Peria, M. and Sanchez,S. (2002). Bank
lending to small businesses in Latin America: Does bank
origin matter? Mimeo, The World Bank, Washington, D.C.
• Clarke, G.; Cull,R. ; Amato, L. and Molinari, A. (1999). The
effect of foreign entry on Argentina’s domestic banking
sector, Policy Research Working Paper 2158, The World
Bank, Washington, D.C.
• Coleman, L. (2007). Risk and decision making by finance
executives: a survey study. International Journal of
Managerial Finance, 3(1), 108-124.
• Cornett, M.; Guo, M L., Khaksari, S., Tehranian, H. (2000).
Performance differences in privately- owned versus state-
owned banks: an international comparison, Working Paper,
World Bank.
• DeMeza, D. and Webb, D. (2000). Does Credit Rationing
Imply Insufficient Lending? Journal of Public Economics,
78, 215-234.
• Donaldson, G (1992). Costly Liquidation, Interbank Trade,
Bank Runs and Pan-ics. Journal of Financial
Intermediation, 2, 59-82.
• Eichengreen, B. (1999). Towards A New International
Financial Architecture. Institute for International
Economics, Washington, DC.
• Feldman, R. (1997). Small Business Loans, Small Banks,
and a Big Change in Technology Called Credit Scoring.
Federal Reserve Bank of Minneapolis. The Region, 19-25.
• Gilbert, R.A. and Wilson, P.W. (1998). Effects of regulation
on productivity of Korean Banks. Journal of Economics and
Business, 50,133–155.
• Godlewski, C. (2006). Regulatory and Institutional
Determinants of Credit Risk Taking and a Bank's Default in
Emerging Market Economies A Two-Step Approach.
Journal of Emerging Market Finance, sage publications,
5(2), 183-206.
• Gorton, G. and Frank, S. (1999). Corporate governance,
ownership dispersion and efficiency: Empirical.
• Havrylchyk, O. (2003). Efficiency of the Polish Banking
Industry: Foreign versus National Banks, Working Paper,
Department of Economics, European University Viadrina
Frankfurt (Oder), Grobe Scharrnstrabe 59, 15230 Frankfurt
(Oder), Germany.
• Hirshey, Mark (1999). Managerial equity ownership and
bank performance. Economic Letters, 64, 209-13.
International Monetary Fund (2000). Bulgaria: Selection
issues and statistical appendix. IMF Staff Country Report,
00/54.
• Jaffee, Dwight, and T. Russell, (1976). Imperfect
Information and Credit Rationing. The Quarterly Journal of
Economics. 90(4), 651-56.
• Jayadev, M. (2006). Internal Credit Rating Practices of
Indian Banks. Money, Banking and Finance, 41(11).
• Kiyotaki, N. and J. Moore. (1997). Credit Cycles. Journal of
Political Economy, 105 (2), 211-48.
• Kwan, S and Eisenbis, R. (1997). Bank Risk, Capitalisation
and Operating Efficiency. Journal of Financial Services
Research, 12, 117–31.
• La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny
(2002). Investor Protection and Corporate Valuation.
Journal of Finance, 57, 1147-1170.
• Lowe, P (2002). Credit risk measurement and procyclicality.
Bank of International settlements - Monetary and Economic
Department, Working Papers (No 116).
• Mehrotra,S. (2005). IBA Bulletin , Indian Bank’s
Association.
• Majnoni,G.; Miller,M. and Powell,A. (2004). Bank Capital
And Loan Loss Reserves Under Basel II: Implications For
Emerging Countries. World Bank Policy Research Working
Paper 3437, Washington, DC.
• Meyer, L. (2000). Why Risk Management Is Important for
Global Financial Institutions” at the Risk Management of
Financial Institutions United Nations Conference Center
Bangkok, Thailand.
• Nunnally, C.J. (1978). Psychometric Theory. McGraw-
Hill, New York, NY.
• Oldfield, G.S. and Santomero, A.M. (1997). Risk
Management in Financial Institutions. Sloan Management
Review, 39(1), 33-46.
• Salas, V. and Saurina, J. (2002). Credit risk in two
institutional regimes: Spanish commercial and savings
banks. The Journal of Financial Services Research, 22 (3),
203-16.
• Sarkar, Jayati, Subrata Sarkar and Sumon K. Bhaumik
(1998). Does ownership always matter? Evidence from the
Indian banking industry? Journal of Comparative
Economics, 26, 262-281.
• Selltiz, C., Wrightsman, L.S. and Cook, W. (1976). Research
Methods in Social Relations. Holt, Rinehart and Winston,
New York, NY.
• Stiglitz, Joseph, and Andrew Weiss (1981). Credit
Rationing in Markets with Imperfect Information.
American Economic Review. 71, 393-410.
31
Credit Risk Analysis in Indian Commercial Banks-An Empirical Investigation
• Vives, Xavier (1990). Banking Competition and European
Integration. CEPR Discussion Papers, 373.
32
• Wesley, D. (1993). Credit Risk Management: Lessons for
Success. Journal of Commercial Lending, 08.
Appendix
Bank’s Risk Management ScaleAuthors-Al-Tamimi and Al-Mazrooei (2007)
InstructionsPlease read the questions carefully and mark (X) at the appropriate place in one of the five columns, as the case may be. The questionnaire is designed to know your opinion in general. Please note it is not to test policies of your banks. There is no right or wrong answer. The data is being collected for purely academic purpose.
General InformationName of the Bank :Name of the employee (optional) :Designation :
STATEMENT Strongly
Disagree
Disagree Neutral Agree Strongly
Agree
1. This bank undertakes a credit
worthiness analysis
before granting loans
2. Before granting loans your bank
undertake a specific
analysis including the client’s characters, capacity,
collateral capital and conditions
3. This banks’ borrowers are classified
according to a risk factor (risk rating)
4. It is essential to require sufficient
collateral from the small borrowers
5. This bank’s policy requires collateral for all granting
loans
6. It is preferable to require collateral against some
loans and not all of them
7. The level
of credit granted to
defaulted clients must be reduced
Table 1: Result of the KMO and Bartlett’s test for Risk Assessment and Analysis
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.676
Bartlett’s test of Sphericity
Approx. chi square
728.998
df
21
Sig.
0.000
Swaranjeet Arora
Table 2: Rotated Factor Matrix for Credit Risk Analysis
Var. No. F1 F2 Communalities
V1
0.815
0.669
V2
0.842
0.792
V3
0.775
0.681
V4
0.913
0.838
V5
0.887
0.822
V6
0.687
0.593
V7
0.770
0.664
Eigen value
3.46
1.60
Cumulative variance
44.00
72.29
33
Note: F1 and F2 are two derived factors.
Table 3: Factors of Credit Risk Analysis
Sl. No.
FACTOR Item
Item Item Item Item
1 Credit Worthiness Analysis
Clients’ Character, capacity, capital, collateral
and conditions Analyses (3.9)
Credit worthiness Analysis (3.8)
Risk Rating (4.2)
Less credit to defaulted clients (3.8)
Collateral against some loans (3.8)
2 Collateral Requirements
Collateral requirements from small borrowers (3.6)
Collateral for all granting loans (3.4)
The figures in parenthesis represent the average scores for the variables under each Factor that determine Credit Risk Analysis.
Table 4: Results of One Way ANOVA Credit Risk Analysis
Sum of Squares
Df Mean Square
F Sig.
Between Groups
23.99608
3 7.998693
26.24193
.000
Within Groups
59.74194
196 0.305
Total 83.73802 199
*The mean difference is significant at the 0.01 level.
Credit Risk Analysis in Indian Commercial Banks-An Empirical Investigation
Table 5: Post Hoc Test-Credit Risk Analysis
Mean Difference (I-J)
Std. Error
Sig. 95% Confidence Interval
(I) (J) Lower Bound
Upper Bound
Tukey HSD
SBI NEW PVT PUB OLD PVT
-0.424 0.2098 -0.672
0.1104180.1104180.110418
.001
.231
.000
-0.710 -0.076 -0.959
-0.138 0.496
-0.387
NEW PVT
SBI PUB OLD PVT
0.4244 0.6342 -0.248
0.1104180.1104180.110418
.001
.000
.114
0.138 0.348
-0.535
0.711 0.920 0.038
PUB SBI NEW PVT OLD PVT
-0.209 -0.634 -0.882
0.1104180.1104180.110418
.231
.000
.000
-0.496 -0.920 -1.169
0.076 -0.348 -0.596
OLD PVT
SBI NEW PVT PUB
0.6728 0.2484 0.8826
0.1104180.1104180.110418
.000
.114
.000
0.387 -0.038 0.596
0.959 0.535 1.169
34
*The mean difference is significant at the .05 level.
Swaranjeet Arora