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Banks Profitability in Pakistan
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Khadim Ali Shah Bukhari Institute of
Technology A thesis on
“Rising Non-performing Loans and Banks Profitability. Analytical study of Credit Risk Assessment policies under SBP
regulations.
Bank AL Habib Ltd as a case study from 2005 to date”
Submitted to: Faculty of Management Sciences
In partial fulfillment of the requirements for the degree of
MASTER OF BUSINESS ADMINISTRATION (MBA)
Submitted By:
Salman Qadir
ID: 4514
Major Subject: Finance
DEDICATION I am dedicating this research to my beloved parents who did not deprive me from benefiting and
having the light of education.
I would like to express my indebtedness to my parent for their intangible sacrifices and
uninterrupted inspiration during my research work.
ACKNOWLEDGEMENT
This thesis has been the result of research conducted during 2011 within the division of the
Department of Management Sciences at KASBIT, Karachi.
All the praise is for Allah, the most merciful and beneficent, who blessed me with the
knowledge, gave me the courage and allowed me to accomplish this research.
We gratefully acknowledge Mr. Hamza Khalil Chaudhry for his supervision, advice and
crucial contribution which made him a backbone to this thesis. His involvement with his
originality has triggered and nourished our intellectual maturity that we will benefit from, for a
long time to come.
It is also my immense pleasure to express sincere gratitude to the staff of Bank AL Habib Ltd
whose inspiring guidance, remarkable suggestion, keen interest and constructive criticism helped
me to complete this research efficiently.
I found this research interesting, challenging and most of all rewarding. I hope the report is
informative to anyone who refers to it.
BY Salman Qadir ID No. 4514 Department of Finance, Faculty of Management Sciences, Khadim Ali Shah Bukhari Institute of Technology (KASBIT) Dated: May 14, 2012
ABSTRACT
This research approach provided us the role of NLP’s on banks profitability and credit risk
assessment moreover it explains that how loan growth rate is a basic driver for the non-
performing loans. There has been alarming increase in the amount of non-performing loans
(NPLs) during the last 30 months of the present government. According to the latest figures
available in the Ministry of Finance the non-performing loan are upraised from 10% to 15%.
This study indicates that what are the variables that are influenced loan growth rate as well as it
will determines their impact on drivers of NPL’s. Using the threshold multi regression technique,
we found some evidences that non-performing loans have non-linear effect on banks’ lending
behavior. Each non-performing loan in the financial sector is viewed as an obverse mirror image
of an ailing unprofitable enterprise. From this point of view, the eradication of non-performing
loans is a necessary condition to improve the economic status. If the non-performing loans are
kept existing and continuously rolled over, the resources are locked up in unprofitable sectors;
thus, hindering the economic growth and impairing the economic efficiency.
TABLE OF CONTENTS
Dedication I
Acknowledgement II
Abstract III
CHAPTER # 01
1. Descriptive Introduction to Research 1
1.1 Reason of Topic Selection 1
1.2 Introduction to NPLs 1
1.3 Introduction to Research Topic 2
1.4 Problem Statement 3
1.5 Objective of Research 3
1.6 List of Hypothesis 4
1.7 Alarming Increase of NPLs 5
1.8 Corporate NPLs 5
1.9 Rising NPLs Key Challenges 7
1.10 SBP Prudential Regulation about NPLs 10 (Corporate, SME & Consumer Loans) CHAPTER # 02
2. Literature Review 35
2.1 International and National Studies 35
2.2 Literature Written by Mr. Ishrat Hussain (Governor SBP) 39
2.2.1 Dealing with NPLs of the Banks 39
2.2.2 Proactive Treatment of Stock of NPLs 43
2.2.3 Policy and Regulatory Environment 45
CHAPTER # 03
3. Research Methodology and Research Design 47
3.1 Research Design 47
3.2 Conceptual Frame Work 47
3.2.1 Dependant Variable 47
3.2.2 Independent Variable 47
3.2.3 Moderate Variable 47
3.3 Theoretical Frame Work 48
3.3.1 NPLs in the Banking Industries 50
3.3.1.1 Introduction 50
3.3.1.2 Failure in Management 50
3.3.1.3 First Reason is Credit Management 51
3.3.1.4 Second Reason is Individual / Entities Capacity 51
3.3.1.5 Recommendation 51
3.3.1.6 Conclusion 53
3.4 Research Methodology 53
CHAPTER # 04
4. Statistical Reporting and Data Analysis 55
4.1 Finding and Interpretation of Results 56
4.2 Hypothesis Assessment Summary 61
CHAPTER # 05
5. Conclusion and Recommendations 67
5.1 Conclusion 67
5.2 Recommendation 67
5.3 Future Research 68
5.4 Limitations 68
5.5 Some Important Literature, Discussion and Observations 69
REFERENCES 81 BIBLIOGRAPHY 84
APPENDIX 87
Appendix-1 Bank AL Habib Ltd (BAHL) Data Summary 87
Appendix-2 Profit & Loss Account of BAHL for the year 2011 88
Appendix-3 Profit & Loss Account of BAHL for the year 2009 89
Appendix-4 Profit & Loss Account of BAHL for the year 2007 90
Appendix-5 Profit & Loss Account of BAHL for the year 2005 91
Appendix-6 Profit & Loss Account of BAHL for the year 2003 92
Appendix-7 Profit & Loss Account of BAHL for the year 2001 93
Appendix-8 Balance Sheet of BAHL for the year 2011 94
Appendix-9 Balance Sheet of BAHL for the year 2009 95
Appendix-10 Balance Sheet of BAHL for the year 2007 96
Appendix-11 Balance Sheet of BAHL for the year 2005 97
Appendix-12 Balance Sheet of BAHL for the year 2003 98
Appendix-13 Balance Sheet of BAHL for the year 2001 99
LIST OF TABLES
Table 1.1 Limit & Exposure of Corporate Portfolio 11
Table 1.2 Guidelines in matter of classification of Corporate Portfolio 16
Table 1.3 Limit & Exposure of SME Portfolio 18
Table 1.4 Guidelines in matter of classification of SME Portfolio 24
Table 1.5 Limit & Exposure of Consumer Portfolio 25
Table 1.6 Guidelines in matter of classification of Consumer Portfolio 28
Table 1.7 Guidelines in matter of classification of Credit Card Loan 29
Table 1.8 Guidelines in matter of classification of Auto Loan 31
Table 1.9 Guidelines in matter of classification of Home Mortgage Loan 32
Table 1.10 Guidelines in matter of classification of Personal Loan 34
Table 4.1 Bank AL Habib Data Summary 55
Table 4.2 Regression Analysis 56
Table 4.3 Coefficients 58
Table 4.4 Hypotheses Assessment Summary 61
LIST OF GRAPHS OF BANK AL HABIB LTD Graph # 1 Bar Graph Showing the Trend of Advances 63
Graph # 2 Bar Graph Showing the Trend of Deposits 63
Graph # 3 Bar Graph Showing the Trend of NPLs 64
Graph # 4 Bar Graph Showing the Trend of Profit Before Tax 64
Graph # 5 Bar Graph Showing the Trend of Profit After Tax 65
Graph # 6 Line Graph Showing the Trend of Profit Before Tax and NPLs 65
Graph # 7 Line Graph Showing the Trend of Profit After Tax and NPLs 66
Graph # 8 Line Graph Showing the % of NPLs to Advances Trend 66
Chapter # 01
DESCRIPTIVE INTRODUCTION TO RESEARCH
1.1 REASON OF TOPIC SELECTION:
As per the World Bank analysis provided in 2002 and 2010, the non-performing loans are
continuously increasing debtor’s rate of default is elaborating a high degree of curve therefore
the researcher came up with idea to evaluate empirically that what the relationship between SBP
regulations and non-performing loans as well as researcher wants to elaborate the reasons of
NPLs by formulating its relationship with the credit risk assessment policies
1.2 INTRODUCTION TO NPL’S
A Non-performing loan is a loan that is in default or close to being in default. Many loans
become non-performing after being in default for 3 months, but this can depend on the contract
terms. A loan is nonperforming when payments of interest and principal are past due by 90 days
or more, or at least 90 days of interest payments have been capitalized, refinanced or delayed by
agreement, or payments are less than 90 days overdue, but there are other good reasons to doubt
that payments will be made in full.
1.3 INTRODUCTION TO RESEARCH TOPIC:
There has been alarming increase in the amount of non-performing loans (NPLs) during the last
30 months of the present government. According to the latest figures available in the Ministry of
Finance the stock of NPLs to Rs. 290 billion by end 2001 from Rs. 207 billion in 2000 and Rs.
185 billion in the year 1999. According to the latest country report of the World Bank this
amount may increase to Rs 384 billion by end of the current year. Recovery of stuck up loans
was one of the prime objectives of General Musharraf, when he took over in October 1999, the
then previous government. The recovery drive launched by the National Accountability Bureau
(NAB) made wholesale arrests of the businessmen, even on circumstantial defaults, resulting in
severe backlash and further loss of confidence. NAB was able to recover few billions rupee from
the erring industrialists, but during the process many industries fell sick. The contracting
economic activity in the country fuelled further failures, and more loans were categorized as
non-performing. Despite the official claims of over Rs. 50 billion cash recoveries, the Finance
Ministry figures show an increase of almost Rs. 105 billion in the NPLs during the present
regime. Realizing its dismal performance on the recovery of defaulted loans the government took
a fresh initiative in this regard. The then President Rafiq Tarar promulgated an ordinance to
provide for expeditious legal remedies for the matters relating to non-performing assets. The
ordinance also provided for legal remedies for the recovery of outstanding loans of the banks and
other financial institutions to make them attractive for privatization and to promote the
revitalization of national economy and rehabilitation and restructuring undertakings. The
ordinance was expected to enable the government to expedite the process of recovery of
outstanding loans as it provided for the high courts to set up special tribunals to deal with the
loan defaulters. However like previous attempts and actions, this effort has also failed to bring
about any significant improvement in the situation. According to the latest country report of
World Bank (2002) Pakistan Development Policy Review the non-Performing Loans (NPLs) of
the Nationalized Commercial Banks (NCBs) have become a major problem because of political
interference and directed credits to individuals and companies. The amount of NPLs have
reached alarming proportions, that is, Rs. 384 billion in the current fiscal year. It is supposed to
be one of the strangest
1.4 PROBLEM STATEMENT:
The non-performing loans are continuously increasing debtor’s rate of default is elaborating a
high degree of curve therefore the researcher came up with idea to evaluate empirically that what
the relationship between SBP regulations and non-performing loans as well as researcher wants
to elaborate the reasons of NPLs by formulating its relationship with the credit risk assessment
policies
1.5 OBJECTIVES OF RESEARCH:
To determine the reasons of non-performing loans on the basis of debtor’s evaluation that
is based on the SBP’s Policies and his past credit history.
The role of KIBOR’s fluctuation on the credit risk assessment and to forecast its impact
on the debtor’s credit historical evaluation.
To review and advise the modifications in the policies given by the collaboration of
Banks and SBP to identify future credit risk and to prevent the future losses via NPL’s.
1.6 LIST OF HYPOTHESIS:
Hypothesis No. 1
Ho: There is no significant relationship with loan growth rate and deposit growth rate.
H1: There is significant relationship with loan growth rate and deposit growth rate.
Hypothesis No. 2
Ho: There is no significant relationship with loan growth rate and capital growth rate.
H1: There is significant relationship between loan growth rate and capital growth rate.
Hypothesis No. 3
Ho: There is no positive relationship other asset growth rate and loan growth rate.
H1: There is positive relationship other asset growth rate and loan growth rate.
Hypothesis No. 4
Ho: There is no positive relationship between bank profitability and inflation rate.
H1: There is a positive relationship between bank profitability and inflation rate.
Hypothesis No. 5
Ho: There is no positive relationship between loan growth rate and NPL’s.
H1: There is positive relationship between loan growth rate and NPL’s.
1.7 ALARMING INCREASE IN NON-PERFORMING LOANS
The banking system of Pakistan remained distressed during the time period, July-Sept 2009 as
expressed by the State Bank banking performance review for the said period. In the quarter under
assessment, the non-performing loans (NPLs) ascended by 6 percent to Rs 452 billion in
comparison to the previous quarter, April-June 2009 while, net NPLs to net loans ratio remained
at 4.1 per cent. Rising non-performing loans are hampering the profitability of Pakistani’s
banking sector. Many reasons are causing this alarming rise but, primarily high interest rate,
economic slowdown and poor law and order situation in the country are the main factors. Non-
payments are increasing in all fields but mostly from the agriculture and government sector.
Though, the interest rate has been reduced to 12.45 percent by SBP, yet the industrialists are
unsatisfied by this decrease and find it difficult to repay their loans due to lower production and
reduced repayment capacity. Currently, the law and order situation is hitting business activities
badly which in result, is hindering the repayment potential.
1.8 CORPORATE NON-PERFORMING LOANS
The ailing textile sector consuming loans up to approximately Rs543.8 billion till October 19,
2009, can be a big hazard for banks. As the chances of recovery of these loans seem lesser now
because the borrowers have either defaulted or are currently near default. The risk of the banks
is also high because, only 0.5 per cent of the total borrowers of the Pakistani banks with loan size
of more than Rs10 million are consuming 71.7 per cent of total bank credit portfolio, a fact
recently acknowledged by the State Bank. The ever-increasing terrorism threat is another factor
hindering the business activities in the economy along with the persisting energy shortfall, which
has also severely added to the disaster of the banking sector on the loan recovery front. Many
banks were involved in consumer financing, now the bursting bubble of consumer financing has
caused an immense hike in default rates. On the other hand recovered collaterals have been
perished or became worthless for banks. Thus, the consumer banking assortment has a major
contribution in the upward trend of NPLs. Exporters of the country are now demanding for
extension in repayment as they have been stuck into the dilemma of lower demand at global level
and a slowdown in production at local level due to poor law and order situation and electricity
outages. However, State Bank of Pakistan is dealing with the situation in a comprehensive
manner through many tools like; improvement in coverage and reporting of NPLs, a proactive
treatment of the existing stock of NPLs, stemming flow of new NPLs and improving the policy
and regulatory environment. The bank is of the view that the stock of existing NPLs will always
grow over time, even if all the new loans being granted are fully serviced. This will happen
because the declared amount consists of principal and mark up. By its very definition, if the loan
is not being serviced and is overdue by 90 days then the unrealized markup will continue to be
added up to the total amount of NPLs. Moreover, some of the commercial banks’ foreign
branches had granted loans in foreign currency. Now, the overdue amount is escalating
automatically with the increase in exchange rate of dollar. State Bank of Pakistan is striving
seriously to tackle the problem, as a measure, exporters have been allowed an extension of one
year for their loans repayment. It has decided to distinguish between willful defaulters and
circumstantial defaulters. The cases of willful defaulters have been referred to the National
Accountability Bureau (NAB) for action under the NAB Ordinance. On September 27, 2007 the
NAB Punjab had filed a reference in an accountability court against 12 people including six
officials of Bank of Punjab (BoP) and six others accused of Rs9 billion fraud. Total of Rs18
billion loans written-off from the government-owned banks during the first three years of the
military government, will be also recovered soon.
Despite these positive steps, the interest rate which is causing lower capacity of repayment of
industrialists, should also be decreased in order to guarantee the repayments of due amounts.
Moreover, cost of production should be brought to the normal level which is nowadays, on hike
due to high cost of electricity, gas and petroleum products. On the other hand banks should
further tighten their credit assessment security policy and arrange appropriate monitoring
procedures in order to keep an eye on NPLs. In the prevailing circumstances, the banks should
avoid financing against high risk securities. It is a matter of fact that, non-performing loans are
steadily causing lesser profitability of the banking sector. Thus, all the resolving measures for
this dilemma must be on an urgent basis, as the spreads of the banks are shrinking due to the
lower recovery of loans and decreasing yields on lending.
1.9 RISING NON-PERFORMING LOANS KEY CHALLENGES
KARACHI: The biggest challenge for Pakistani banks in 2011 would be the increase in non-
performing loans (NPLs) as higher lending rates and weak economy would give a double
whammy to the borrowers’ payment capacity, analysts said. Heavy flooding in Pakistan during
August 2010 caused a humanitarian disaster and altered macroeconomic outlook, which severely
weakened banks’ operating environment. The direct impact on rated Pakistani banks has been
modest, as the floods plagued largely un-banked rural areas. However, an international rating
agency expects weaker economic growth and higher inflation in the short run, as the floods have
led to food shortages, rising commodity prices and a renewed recourse of the government to
deficit monetization.
“These adverse developments create a difficult operating environment for banks, which is a key
driver of our negative system outlook,” wrote Moody’s analyst Christos Theofilou in its latest
report. We expect these near-term economic challenges to dissipate only gradually through the
first half of 2012, when operating conditions will likely to return to more favorable trends.
Recently, the rating agency revised the ratings of five top banks to negative, which includes
Allied Bank Ltd, Habib Bank Ltd, MCB Bank Ltd, National Bank of Pakistan and the United
Bank Ltd. However, some investors foresee an early recovery in the banking sector. Quarterly
analysis shows even better results as the banking sector fought well with the flood incident and
now seems to be on the recovery stage, registering 4.7 percent quarter-on-quarter (Q-o-Q)
growth in net advances, whereas deposits grew by five percent Q-o-Q,” an analyst at Invest Cap
said. While investment by the scheduled banks showed 6.2 percent quarterly increase, we see the
incremental impact to ease off as growth in advances realize on the back of recovery from the
present spell,” the analyst said. Another analyst at JS Research said that the recent data for the
sector has been encouraging, and with a sequential Q-o-Q rise in yields. However, we remain
wary of increased provisioning expense, a trend associated with the final quarter,” the analyst
added. The State Bank of Pakistan in its third quarterly report in 2010 on banking performance
said that the heightened credit risk is reflected in a noticeable and persistent increase in the non-
performing loans, doubling over two years by the end of 2009. The growth in NPLs, which
decelerated during the first two quarters of 2010, grew by 7.4 percent during the third quarter to
reach Rs494 billion,” the SBP said. The central bank said that the increased credit risk will
remain a key challenge for banks. There is a need for banks to devise ingenious strategies for
dealing with the high level of non-performing loans so that promising businesses, facing
transitory difficulties only due to a constrained macro environment, continue to contribute in the
economic growth and service their obligations in an orderly manner.
The usual inventory builds up, particularly by Kharif crop-based industries during the last
calendar quarter, will create additional demand for bank credits. Although the banks are expected
to remain liquid, the heightened demand for credit from the public sector will mean that the
banks’ ability to finance additional private sector loans will be predicated upon mobilization of
fresh deposits and retirement of commodity finance by the government-owned agencies, which
continues to be extremely high, the SBP said. “Banks will need to reduce their large portfolio of
government paper and lending to the public sector agencies so as to reduce their sovereign
exposure, as well as to make credit available to the private sector for maintaining economic
growth and, thereby, enhance and diversify revenues of the banking system,” it added. However,
the central bank said that the aggregate earnings of the system are expected to be satisfactory,
although these will continue to be concentrated in banks endowed with a wide network and
competitively better placed to raise stable and relatively cheap funds. In addition, persistent
macro-environment issues will pose a stiff challenge for some banks to enhance their minimum
capital requirement to Rs 8 billion by the end of 2011.
1.10 SBP- PRUDENTIAL REGULATION ABOUT NON-PERFORMING
LOANS
CORPORATE PRUDENTIAL REGULATIONS
REGULATION R-1
(Limit On Exposure to a Single Person / Group)
The total outstanding exposure (fund based and non-fund based) by a bank/DFI to any single
person shall not at any point in time exceed 30% of the bank’s/DFI’s equity as disclosed in the
latest audited financial statements, subject to the condition that the maximum outstanding against
fund based exposure does not exceed 20% of the bank’s/DFI’s equity.
The total outstanding exposure (fund based and non-fund based) by a bank/DFI to any group
shall not exceed 50% of the bank’s/DFI’s equity as disclosed in the latest audited financial
statements, subject to the condition that the maximum outstanding against fund based exposure
does not exceed 35% of the bank’s/DFI’s equity.
Limit on exposure to a single person/Group effective from 31-12-2009 and onward would be as
under:
Table 1.1
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
The group will cover both corporate entities as well as SMEs, in cases where such entities are
owned by the same group.
REGULATION R-8
(Classification and Provisioning for Assets Loans/Advances)
Banks/DFIs shall observe the prudential guidelines given by state bank of Pakistan in the matter
of classification of their asset portfolio and provisioning there-against.
In addition to the time-based criteria prescribed in Annexure-IV, subjective evaluation of
performing and non-performing credit portfolio shall be made for risk assessment and, where
considered necessary, any account including the performing account will be classified, and the
category of classification determined on the basis of time based criteria shall be further
downgraded. Such evaluation shall be carried out on the basis of credit worthiness of the
borrower, its cash flow, operation in the account, adequacy of the security, inclusive of its
realizable value and documentation covering the advances.
The rescheduling/restructuring of non-performing loans shall not change the status of
classification of a loan/advance etc. unless the terms and conditions of rescheduling/restructuring
are fully met for a period of at least one year (excluding grace period, if any) from the date of
such rescheduling/restructuring and at least 10% of the outstanding amount is recovered in cash.
However, the condition of one year retention period, prescribed for restructured/rescheduled loan
account to remain in the classified category, will not apply in case the borrower has repaid or
adjusted in cash at least 50% of the total restructured loan amount (principal + markup), either at
the time of restructuring agreement or later-on during the grace period if any.
The unrealized mark-up on loans (declassified after rescheduling/restructuring) shall not be taken
to income account unless at least 50% of the amount is realized in cash. However, any short
recovery in this respect will not impact the declassification of this account if all other criteria
(meeting the terms and conditions for at least one year and payment of at least 10% of
outstanding amount by the borrower) are met. The banks/DFIs are further directed to ensure that
status of classification, as well as provisioning, is not changed in relevant reports to the State
Bank of Pakistan merely because a loan has been rescheduled or restructured.
However, while reporting to the Credit Information Bureau (CIB) of State Bank of Pakistan,
such loans/advances may be shown as ‘rescheduled/restructured’ instead of ‘default’. Where a
borrower subsequently defaults (either principal or mark-up) after the rescheduled/restructured
loan has been declassified by the bank/DFI as per above guidelines, the loan will again be
classified in the same category it was in at the time of rescheduling/restructuring and the
unrealized markup on such loans taken to income account shall also be reversed. However,
banks/DFIs at their discretion may further downgrade the classification, taking into account the
subjective criteria. At the time of rescheduling/restructuring, banks/DFIs shall consider and
examine the requests for working capital strictly on merit, keeping in view the viability of the
project/business and appropriately securing their interest etc. All fresh loans granted by the
banks/DFIs to a party after rescheduling/ restructuring of its existing facilities may be monitored
separately, and will be subject to classification under this Regulation on the strength of their own
specific terms and conditions.
Banks/DFIs shall classify their loans/advances portfolio and make provisions in accordance with
the criteria prescribed above, keeping in view the following:
Banks are allowed to take the benefit of 40% of Forced Sale value (FSV) of the pledged stocks
and mortgaged residential, commercial and industrial properties (where building is constructed)
held as collateral against NPLs for three years from the date of classification for calculating
provisioning requirement. However, the banks/DFIs can avail the benefit of 40% of FSV of
mortgaged residential, commercial and industrial land (open plot and where building is
constructed separate valuation of land must be available) held as collateral against NPLs for four
years from the date of classification for calculating provisioning requirement. This benefit would
be available in such cases where FSV valuation of land is not more than four years old. For the
purpose of determination of FSV, revised Annexure-V of PR for Corporate/Commercial Banking
shall be followed.
Banks/DFIs may avail the above benefit of FSV subject to compliance with the following
conditions:
The additional impact on profitability arising from availing the benefit of FSV against pledged
stocks and mortgaged residential, commercial and industrial properties (land and building only)
shall not be available for payment of cash or stock dividend.
Heads of Credit of respective banks/DFIs shall ensure that FSV used for taking benefit of
provisioning is determined accurately as per guidelines contained in PRs and is reflective of
market conditions under forced sale situations.
Party-wise details of all such cases where banks/DFIs have availed the benefit of FSV shall be
maintained for verification by State Bank’s inspection teams during regular /special inspection.
Any misuse of FSV benefit detected during regular /special inspection of SBP shall attract strict
punitive action under the relevant provisions of the Banking Companies Ordinance, 1962.
Furthermore, SBP may also withdraw the benefit of FSV from banks/DFIs found involved in its
misuse.
TIMING OF CREATING PROVISIONS:
Banks/DFIs shall review, at least on a quarterly basis, the collectability of their loans/advances
portfolio and shall properly document the evaluations so made. Shortfall in provisioning, if any,
determined, as a result of quarterly assessment shall be provided for immediately in their books
of accounts by the banks/DFIs on quarterly basis.
REVERSAL OF PROVISION:
In case of cash recovery, other than rescheduling/restructuring, banks/DFIs may reverse specific
provision held against classified assets, subject to the following:
(a) In case of Loss account, reversal may be made to the extent that the remaining outstanding
amount of the classified asset is covered by minimum 100%
provision.
(b) In case of Doubtful account, reversal may be made to the extent that the remaining
outstanding amount of the classified asset is covered by minimum 50% provision.
(c) In case of Substandard account, reversal may be made to the extent that the remaining
outstanding amount of the classified asset is covered by minimum 25% provision. While
calculating the remaining provision required to be held after cash recovery and reversal of
provision there-against, the banks/DFIs will enjoy the benefit of netting-off the amount of liquid
assets from the outstanding amount, in the light of guidelines given in this regulation. However,
the provision made on the advice of State Bank of Pakistan will not be reversed without prior
approval of State Bank of Pakistan.
Table 1.2
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
SME (SMALL & MEDIUM ENTERPRISES)
PRUDENTIAL REGULATIONS
REGULATION R-6
(Per Party Exposure Limit)
The maximum exposure of a bank/DFI on a single SME shall not exceed Rs 75 million. The total
facilities (including leased assets) availed by a single SME from the financial institutions should
not exceed Rs 150 million provided that the facilities excluding leased assets shall not exceed Rs
100 million. It is expected that SMEs approaching this limit should have achieved certain
sophistication as they migrate into larger firms and should be able to meet the requirements of
Prudential Regulations for Corporate/Commercial Banking.
REGULATION R-7
(Aggregate Exposure of a Bank/DFI on SME Sector)
The aggregate exposure of a bank/DFI on SME sector shall not exceed the limits as
specified below:
Table 1.3
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
REGULATION R-11
(Classification and Provisioning for Assets Loans/Advances)
Banks/DFIs shall observe the prudential guidelines given at Annexure-III in the matter of
classification of their SME asset portfolio and provisioning there-against. In addition to the time-
based criteria prescribed in Annexure-III, subjective evaluation of performing and non-
performing credit portfolio shall be made for risk assessment and, where considered necessary,
any account including the performing account will be classified, and the category of
classification determined on the basis of time based criteria shall be further downgraded. Such
evaluation shall be carried out on the basis of credit worthiness of the borrower, its cash flow,
operation in the account, adequacy of the security, inclusive of its realizable value and
documentation covering the advances. The rescheduling/restructuring of non-performing loans
shall not change the status of classification of a loan/advance etc. unless the terms and conditions
of rescheduling/restructuring are fully met for a period of at least one year (excluding grace
period, if any) from the date of such rescheduling/restructuring and at least 10% of the
outstanding amount is recovered in cash. However, the condition of one year retention period,
prescribed for restructured/rescheduled loan account to remain in the classified category, will not
apply in case the borrower has repaid or adjusted in cash at least 50% of the total restructured
loan amount (principal + markup), either at the time of restructuring agreement or later-on during
the grace period.
The unrealized mark-up on loans (declassified after rescheduling/restructuring) shall not be taken
to income account unless at least 50% of the amount is realized in cash. However, any short
recovery in this respect will not impact the declassification of this account if all other criteria
(meeting the terms and conditions for at least one year and payment of at least 10% of
outstanding amount by the borrower) are met. The banks/DFIs are further directed to ensure that
status of classification, as well as provisioning, is not changed in relevant reports to the State
Bank of Pakistan merely because a loan has been rescheduled or restructured. However, while
reporting to the Credit Information Bureau (CIB) of State Bank of Pakistan, such loans/advances
may be shown as ‘rescheduled/restructured’ instead of default.
Where a borrower subsequently defaults (either principal or mark-up) after the
rescheduled/restructured loan has been declassified by the bank/DFI as per above guidelines, the
loan will again be classified in the same category it was in at the time of
rescheduling/restructuring and the unrealized markup on such loans taken to income account
shall also be reversed. However, banks/DFIs at their discretion may further downgrade the
classification, taking into account the subjective criteria. At the time of
rescheduling/restructuring, banks/DFIs shall consider and examine the requests for working
capital strictly on merit, keeping in view the viability of the project/business and appropriately
securing their interest etc.
All fresh loans granted by the banks/DFIs to a party after rescheduling/restructuring of its
existing facilities may be monitored separately, and will be subject to classification under this
Regulation on the strength of their own specific terms and conditions.
Banks/DFIs shall classify their loans and advances portfolio and make provisions in accordance
with the criteria prescribed above, keeping in view the following:
Banks/DFIs are allowed to take the benefit of 40% of Forced Sale Value (FSV) of the pledged
stocks and mortgaged residential, commercial and industrial properties (building only) held as
collateral against NPLs for three years from the date of classification for calculating provisioning
requirement. However the banks/ DFIs can avail benefit of 40% of FSV of mortgaged
residential, commercial and industrial land (open plot, and where building is constructed separate
valuation of land must be available) held as collateral against NPLs for four years from the date
of classification for calculating provisioning requirement. This benefit would be available in
such cases where FSV valuation of land is not more than four years old. For the purpose of
determination of FSV, Annexure-IV of PR for SME Financing shall be followed.
Banks/DFIs may avail the above benefit of FSV subject to compliance with the following
conditions:
i) The additional impact on profitability arising from availing the benefit of FSV against the
pledged stocks and mortgaged residential, commercial and industrial properties shall not be
available for payment of cash or stock dividend.
ii) Heads of Credit of respective banks/DFIs shall ensure that FSV used for taking benefit of
provisioning is determined accurately as per guidelines contained in PRs and is reflective of
market conditions under forced sale situations
iii) Party-wise details of all such cases where banks/DFIs have availed the benefit of FSV shall
be maintained for verification by State Bank’s inspection teams during regular/special
inspection.
c) Any misuse of FSV benefit detected during regular/special inspection of State Bank shall
attract strict punitive action under the relevant provisions of the Banking Companies Ordinance,
1962. Furthermore, State Bank may also Withdraw the benefit of FSV from banks/DFIs found
involved in its misuse.
In order to provide relief to the borrowers of flood affected areas identified by the National
Disaster Management Authority (NDMA), banks/DFIs are encouraged to reschedule/restructure
Agriculture and SME loans/advances to such borrowers, as per existing Prudential Regulations
(PRs) of Agriculture and SME Financing where the possibility of recovery exists. For all such
rescheduled/restructured loans and advances, Banks/DFIs may defer loan provisioning up to 31st
December 2011. However, classification of such loans shall be done as per criteria laid down in
the relevant PRs. This relaxation is available for loans and advances which have become non-
performing since July 1, 2010 in the affected areas identified by NDMA. Loans/advances
classified before this date shall not qualify for this relaxation.
TIMING OF CREATING PROVISIONS:
Banks/DFIs shall review, at least on a quarterly basis, the collectability of their loans/advances
portfolio and shall properly document the evaluations so made. Shortfall in provisioning, if any,
determined, as a result of quarterly assessment shall be provided for immediately in their books
of accounts by the banks/DFIs on quarterly basis.
REVERSAL OF PROVISION:
In case of cash recovery, other than rescheduling/restructuring, banks/DFIs may reverse specific
provision held against classified assets, subject to the following:
i) In case of Loss account, reversal may be made to the extent that the remaining outstanding
amount of the classified asset is covered by minimum 100%provision.
ii) In case of Doubtful account, reversal may be made to the extent that there main outstanding
amount of the classified asset is covered by minimum 50% provision.
iii) In case of substandard account, reversal may be made to the extent that the remaining
outstanding amount of the classified asset is covered by minimum 25% provision.
While calculating the remaining provision required to be held after cash recovery and reversal of
provision there-against, the banks/DFIs will enjoy the benefit of netting-off the amount of liquid
from the outstanding amount, in the light of guidelines given in this regulation. Further, the
provision made on the advice of State Bank of Pakistan will not be reversed without prior
approval of State Bank of Pakistan.
Table 1.4
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
CONSUMER PRUDENTIAL REGULATIONS
REGULATION R-2 (Limit On Exposure Against Total Consumer Financing) Banks/DFIs shall ensure that the aggregate exposure under all consumer financing facilities at
the end of first year and second year of the start of their consumer financing does not exceed 2
times and 4 times of their equity respectively. For subsequent years, following limits are placed
on the total consumer financing facilities:
Table 1.5
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
REGULATION R-3 (Total Financing Facilities to be Commensurate with the Income)
While extending financing facilities to their customers, the banks/DFIs should ensure that the
total installment of the loans extended by the financial institutions is commensurate with
monthly income and repayment capacity of the borrower. In this connection, while determining
the credit worthiness and repayment capacity of the prospective borrower, the banks/DFIs shall
ensure that the total monthly amortization payments of consumer loans should not exceed 50%
of the net disposable income of the prospective borrower1. This measure would be in addition to
banks’/DFIs’ usual evaluations of each proposal concerning credit worthiness of the borrowers,
to ensure that the banks’/DFIs’ portfolio under consumer finance fulfills the prudential norms
and instructions issued by the State Bank of Pakistan and does not impair the soundness and
safety of the bank/DFI itself. Banks/DFIs may waive the requirement of 50% Debt Burden in
case a Credit Card and Personal loan is properly secured through liquid assets (as defined in
prudential regulations) with minimum 30% margin.
REGULATION R-5A
Rescheduling / Restructuring of Non-Performing Consumer Loans:
a) Banks/DFIs should frame policy for rescheduling/ restructuring of non-performing
consumer loans. The policy should be approved by the Board of Directors or by the
Country Head/Executive/Management Committee in case of branches of foreign banks.
b) For the purpose of rescheduling/ restructuring, banks/DFIs may:
Club or consolidate outstanding amounts on account of personal loans and credit cards and create
one loan. The new loan so created shall be placed in the lowest category of classification
amongst the classifications of the loans clubbed. Convert revolving facility into an installment
loan.
Change the tenure of the loan by maximum two years beyond any regulatory cap on maximum
tenure.
c) Rescheduling/ restructuring should not be done just to avoid classification of loans /advances
and provisioning requirements. In this connection, banks /DFIs shall ensure that consumer
financing facilities of any borrower should not be rescheduled/ restructured more than once
within 12 months and three times during five year period.
d) While considering rescheduling/restructuring, banks/DFIs should, interlaid, take into account
the repayment capacity of the borrower. The condition of 50% of Debt Burden Requirement
(DBR) mentioned at Regulation R-3 of Prudential Regulations for Consumer Financing shall not
be applicable to loan rescheduled/ restructured. However, any new consumer financing facility
extended to a borrower who is availing any rescheduled/ restructured facility shall be subject to
observance of minimum DBR prescribed in the Regulation R-3 of Prudential Regulations for
Consumer Financing.
e) The status of classification of the non-performing loans shall not be changed because of
rescheduling / restructuring unless borrower has paid at least 10% of the rescheduled /
restructured amount or six installments as per terms & conditions of the rescheduling/
restructuring. However, for internal monitoring purpose, banks/DFIs may re-set the dpd (days
past due) counter of the newly created loan to “0”.
f) Provisions already held against non-performing loan, to be rescheduled /restructured, will only
be reversed if condition of 10% recovery or six installments is met.
g) If the borrower defaults (i.e. reaches 90 dpd) again within one year after declassification, the
loan shall be classified as under:
Table 1.6
Type of Consumer Classification
Loan
Unsecured Loss
Secured Same category in which it was prior to rescheduling / restructuring.
Banks /
DFIs, however, at their discretion may further downgrade the
classification
based on their own internal policies.
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
CREDIT CARDS PRUDENTIAL REGULATIONS
REGULATION O-1
The banks/DFIs should take reasonable steps to satisfy themselves that cardholders have
received the cards, whether personally or by mail. The banks/DFIs should advise the card holders
of the need to take reasonable steps to keep the card safe and the PIN secret so that frauds are
avoided.
REGULATION O-2
Banks/DFIs shall provide to the credit card holders, the statement of account at monthly
intervals, unless there has been no transaction or no outstanding balance on the account since last
statement.
REGULATION O-3
Banks/DFIs shall be liable for all transactions not authorized by the credit card holders after they
have been properly served with a notice that the card has been lost/stolen. However, the
bank’s/DFI’s liability shall be limited to those amounts wrongly charged to the credit card
holder’s account. In order to mitigate the risks in this respect, the banks/DFIs are encouraged to
take insurance cover against wrongly charged amounts, frauds, etc.
The bank/DFI shall, however, not charge the borrowers’ account with any amount under the head
of “insurance premium” (by what so ever name called) without obtaining consent of each
existing & prospective customer in writing. In addition to obtaining consent in writing, the
banks/DFIs may also use the following modes for obtaining prior consent of their customers
provided proper record is maintained by banks/DFIs.
REGULATION R-8 (CLASSIFICATION AND PROVISIONING)
The credit card advances shall be classified and provided for in the following manner:
Table 1.7
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
It is clarified that the lenders are allowed to follow more conservative policies. Further,
provisioning may be created and maintained by the Bank / DFI on a portfolio basis provided that
the provision maintained by the Bank / DFI shall not be less that the level required under this
Regulation.
AUTO LOANS PRUDENTIAL REGULATIONS
REGULATION R-9 The vehicles to be utilized for commercial purposes shall not be covered under the Prudential
Regulations for Consumer Financing. Any such financing shall ensure compliance with
Prudential Regulations for Corporate/Commercial Banking or Prudential Regulations for SMEs
Financing. These regulations shall only apply for financing vehicles for personal use including
light commercial vehicles also used for personal purposes.
REGULATION R-10 The maximum tenure of the auto loan finance shall not exceed seven years.
REGULATION R-14 The auto loans shall be classified and provided for in the following manner:
Table 1.8
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
HOME MORTGAGE LOAN PRUDENTIAL REGULATIONS REGULATION R-16 The housing finance facility shall be provided at a maximum debt-equity ratio of 85:15. REGULATION R-22
The mortgage loans shall be classified and provided for in the following manner:
Table 1.9
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
PERSONAL LOAN PRUDENTIAL REGULATIONS
(Regulations For Personal Loans Including Loans For The Purchase Of Consumer
Durables)
REGULATION R-23
(Clean Limit Per Person for Personal Loans)
Banks/DFIs may assign personal loan limits to one person with a maximum unsecured limit not
exceeding Rs 1,000,000/, subject to mandatory credit check & prescribed debt burden and
condition that total unsecured personal loans limits availed by that person from all banks/DFIs
does not exceed Rs. 1,000,000. Banks/DFIs may merge the clean limits to single person for
Personal Loans and Credit Cards subject to the condition that total clean limit availed by him/her
from all banks/DFIs does not exceed Rs. 2,000,000 at any point in time. It is re-emphasized that
the aggregate clean limit of the borrower should not exceed Rs. 2,000,000.
Banks/DFIs shall ensure that overall personal loan limits and credit card limits, both on secured
as well as on unsecured basis, availed by one person from all banks/DFIs in aggregate should not
exceed Rs 5,000,000/-, at any point in time, subject to the condition that the overall
unsecured/clean facilities on account of personal loan and credit card of that individual does not
exceed Rs 2,000,000. The loan secured against liquid securities shall, however, be exempted
from this limit. The loans against the securities issued by Central Directorate of National Savings
(CDNS) shall be subject to such limits as are prescribed by CDNS/Federal Government/State
Bank of Pakistan from time to time.
REGULATION R-27
The personal loans shall be classified and provided for in the following manner:
Table 1.10
(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)
Chapter # 02
LITERATURE REVIEW
2.1 INTERNATIONAL AND NATIONAL STUDIES
Corresponding author Department of Economics, University of Birmingham Edgbaston
Birmingham
The non-performing loan problem in commercial banks. Using the threshold regression
technique, we found some evidences that non-performing loans have non-linear negative effect
on bank lending behavior. On the contrary, when banks have non-performing loans lower than
the threshold; they are less regressive in increasing lending as suggested by the estimated
coefficients. However, when non-performing loan rates are under the threshold level, non-
performing loans have positive impacts banks’ lending behavior with a statistically significant
positive coefficient 0.4909. It suggests that banks may still increase their loans as the generation
of non-performing loans is the natural result of lending, especially for banks in the expansionary
stage.
(Dermirgue-Kunt 1989, Barr and Siems 1994), and that failing banking.
A simple definition of non-performing is: A loan that is not earning income and: full payment of
principal and interest is no longer anticipated, principal or interest is 90 days or more delinquent,
or the maturity date has passed and payment in full has not been made. The issue of non-
performing loans (NPLs) has gained increasing attentions in the last few decades. The immediate
consequence of large amount of NPLs in the banking system is bank failure. Many researches on
the cause of bank failures find that asset quality is a statistically significant predictor of
insolvency (Dermirgue-Kunt 1989, Barr and Siems 1994), and that failing banking.
(Berger and Humphrey (1992), Barr and Siems (1994), DeYoung and Whalen (1994),
Wheelock and Wilson (1994)),
Non-performing loans can lead to efficiency problem for banking sector. It is found by a number
of economists that failing banks tend to be located far from the most-efficient frontier (Berger
and Humphrey (1992), Barr and Siems (1994), DeYoung and Whalen (1994), Wheelock and
Wilson (1994)), because banks don’t optimise their portfolio decisions by lending less than
demanded. What’s more, there are evidences that even among banks that do not fail, there is a
negative relationship between the non-performing loans and performance efficiency (Kwan and
Eisenbeis (1994), Hughes and Moon (1995), Resti (1995)).
United States Council of Economic Advisors (1991)
The phenomena that banks are reluctant to take new risks and commit new loans is described as
the ‘”credit crunch” problem. According to the United States Council of Economic Advisors
(1991), credit crunch is “a situation in which the supply of credit is restricted below the range
usually identified with prevailing market interest rates and the profitability of investment projects
Krueger and Tornell (1999) and Agung et.al. (2001)
Krueger and Tornell (1999) support the credit crunch view and attribute the credit crunch in
Mexico after the 1995 crisis partially to the bad loans. They point out that banks were burdened
with credits of negative real value, thereby reducing the capacity of the banks in providing fresh
fund for new projects. Agung et. al. (2001) using the macro and micro panel data analyses to
study the existence of a credit crunch in Indonesia after the crisis. Both the macro and micro
evidences show that there was a credit crunch, characterized by an excess demand for loans,
starting to emerge in August 1997, one month after the contagion effects of the exchange rate
turmoil in Thailand spreading to Indonesia.
The International Accounting Standard 39 revised in 2003
The International Accounting Standard 39 revised in 2003 focuses on recognition and
measurement of financial instruments and, most importantly, defines and establishes the
measurement and evaluation of impaired loans. As lenders usually make little or no loss
provision for impaired loans, they are at risk to be suddenly forced to reclassify such loans as a
loss and take a full write-down if the borrowers go bankrupt. The initiation of this standard is to
prevent lenders from being caught off-guard. In addition, many global economists, rating
agencies, and organizations such as the World Bank and the Asian Development Bank have
begun to evaluate the effects of NPLs on GDP growth. They reduce growth estimates to reflect
the time and cost of resolving large non-performing loan issues.
Se-Hark Park (2003) “Financial Revival Laws-Based Debt Disclosure” in 1999.
There is no global standard to define non-performing loans at the practical level. Variations exist
in terms of the classification system, the scope, and contents. Such problem potentially adds to
disorder and uncertainty in the NPL issues. For example, as described by Se-Hark Park (2003),
during 1990s, there were three different methods of defining non-performing loans in Japan: the
1993 method based on banking laws; the “Bank’s Self-Valuation” in March 1996; and the
“Financial Revival Laws-Based Debt Disclosure” in 1999.
World Bank report October 1999
According to the latest country report of the World Bank this amount may increase to Rs 384
billion by end of the current year. Recovery of stuck up loans was one of the prime objectives of
General Musharraf, when he took over in October 1999, the then previous government. The
recovery drive launched by the National Accountability Bureau (NAB) made wholesale arrests
of the businessmen, even on circumstantial defaults, resulting in severe backlash and further loss
of confidence. NAB was able to recover few billions rupee from the erring industrialists, but
during the process many industries fell sick.
Rafiq Tarar economy and rehabilitation 2009 Research article Financial Times
The contracting economic activity in the country fuelled further failures, and more loans were
categorized as non-performing. Despite the official claims of over Rs. 50 billion cash recoveries,
the Finance Ministry figures show an increase of almost Rs. 105 billion in the NPLs during the
present regime. Realizing its dismal performance on the recovery of defaulted loans the
government took a fresh initiative in this regard. The then President Rafiq Tarar promulgated an
ordinance to provide for expeditious legal remedies for the matters relating to non-performing
assets. The ordinance also provided for legal remedies for the recovery of outstanding loans of
the banks and other financial institutions to make them attractive for privatization and to promote
the revitalization of national economy and rehabilitation and restructuring undertakings.
Report of World Bank (2002)
The ordinance was expected to enable the government to expedite the process of recovery of
outstanding loans as it provided for the high courts to set up special tribunals to deal with the
loan defaulters. However like previous attempts and actions, this effort has also failed to bring
about any significant improvement in the situation. According to the latest country report of
World Bank (2002) Pakistan Development Policy Review the non-Performing Loans (NPLs) of
the Nationalized Commercial Banks (NCBs) have become a major problem because of political
interference and directed credits to individuals and companies. The amount of NPLs has reached
alarming proportions, that is, Rs. 384 billion in the current fiscal year. It is supposed to be one of
the strangest.
2.2 LITERATURE WRITTEN BY MR. ISHRAT HUSAIN (GOVERNOR STATE BANK
OF PAKISTAN) April 8, 2009.
2.2.1 DEALING WITH NON-PERFORMING LOANS OF BANKS
A lot of confusion and misunderstanding has been created by several commentators on the issue
of non-performing loans (NPLs) of banking system. They take the absolute amount of such loans
at the current point of time and compare it with the quantum of such loans in October 1999 and
make a hue and cry that the situation has deteriorated because the quantum of NPLs has gone up.
Such a simplistic approach creates doubts in the minds of common people about the commitment
of the government and the` State Bank of Pakistan towards recovery of these loans and distorts
the true picture about this important issue. The aim of this article is to inform the public about
the exact magnitude of the problem, the trend over time and the measures the State Bank of
Pakistan is taking to tide over this problem. The State Bank of Pakistan (SBP) is dealing with the
NPL issue in a comprehensive manner through (a) improvement in coverage and reporting of
NPLs (b) a proactive treatment of the existing stock of NPLs (c) stemming flow of new NPLs
and (d) improving the policy and regulatory environment. It should be realized that the stock of
existing NPLs will always grow over time even if all the new loans being granted are fully
serviced. This will happen because the declared amount consists of principal and mark up. By its
very definition, if the loan is not being serviced and is overdue by 90 days then the unrealized
markup will continue to be added up to the total amount of NPLs. For example, if on October
1999 the principal amount due on a NPL was Rs 1 million and the contracted markup rate was 20
percent, then this amount will grow to Rs 1.2 million in October 2000, Rs 1.4 million in October
2001 and Rs 1.6 million in October 2002. So it can be seen that if the principal amounts overdue
to the banking system in October 1999 were Rs 160 billion and these loans fell in the category of
NPLs then three years later they will swell automatically to Rs 256 billion, assuming that the
contracted markup rate was 20 percent per annum. Thus, one can expect that 60 percent increase
will take place in the total quantum of NPLs after a three year period, even if every single new
loan is performing well. The second complication arises if the NPL is denominated in foreign
currency which is the case with 13 percent of all NPLs. These were granted by the foreign
branches of NBP, HBL, UBL and Allied Bank. Assume that these loans were granted when the
rupee-dollar exchange rate was Rs 46 to $ 1 and suppose the principal amount overdue was $ 1
million. At the time the loan was granted, its value on the books of the bank was Rs 46 million.
As per Mr.Ishrat literature in 2008 when the exchange rate is Rs 59, the same NPL will be
shown as Rs 59 billion i.e. 28 percent higher than the original value declared in October 1999.
This excludes the mark-up overdue which will also move up and if this markup is included the
same NPL will be at least 40 percent higher in value in October 2002 (as the dollar mark up rate
has been lower than the rupee markup rate). Thus it should be seen that without any fault of the
bank its aggregate value of NPLs (denominated in foreign currency) has escalated by 40 percent.
(a) Improvement in Coverage and Reporting. The SBP Inspectors have begun to apply more
rigorous standards of classification. In September 2000, the SBP inspectors detected that some of
the public sector specialized banks were reporting only default or overdue portion of their non-
performing loans instead of total outstanding amount of such loans. This led to an upward
revision in the volume of NPLs reported by these banks and resulted in addition of Rs 47 billion
of loans classified as non-performing which were not shown as such in the period prior to
September 2000. Thus overnight the total volume of declared NPLs rose by Rs 47 billion. The
SBP has also revised the valuation method of collaterals underlying the classified loans and
brought them in line with international practices. The banks can now take into account only the
minimum realizable value of assets mortgaged or pledged for determining the provisions. The
realizable value shall be the value that could currently be obtained by selling the
mortgaged/pledged assets in a forced/distressed sale condition. The banks have been asked to
earmark additional provisions against the revised valuations of collaterals. It can be seen from
the above illustrations that the increase in absolute amounts of NPLs cannot be ipso facto
attributed to any deterioration in the underlying quality of assets, but has occurred due to the
stricter enforcement of regulatory, accounting, valuation and prudential rules. Despite the above
factors i.e. addition of unrealized mark up for three years, currency revaluation due to
depreciation of rupee and discovery of undisclosed NPLs, the overall quantum of NPLs by the
end of June 2002 amounted to around Rs 259 billion – an increase of only Rs 47 billion in the
last three years. This increase is equivalent to just the onetime adjustment made in September
2000 to the stock of NPLs due to improvement in the reporting methodology. While the
nationalized commercial banks have brought down their non-performing loans, the largest single
change has been in the category of specialized banks. Their NPLs have risen from Rs 19.3 billion
to Rs 67 billion due to this onetime adjustment. To the banking regulator, it is not the absolute
amount, but the ratios of NPLs to total advances which are the relevant indicators of the quality
of assets and adequacy of capital of the banks. There are two ratios which ought to be monitored
the gross NPLs/gross advances and net NPLs/net advances.
The reason for monitoring these ratios is straight forward and logical. As the banks grant new
loans of good quality after careful appraisal and due diligence, these ratios are bound to decline
over time and the overall quality of assets of the system will improve. More important, it is non-
provisioning of these NPLs which pose a systemic threat to the health of the banking system.
The higher is the provisioning, the lower is the systemic risk. In June 1999, the ratio of gross
NPLs/gross advances of the banks and DFIs was 24 percent and is almost the same today. Had
this under reported amount of Rs 47 billion been added to the portfolio of banks, particularly
ADBP and IDBP, in June 1999 the ratio of gross NPLs / gross advances on comparable basis,
would have been 29 percent. On this basis alone it can be seen that the ratio of gross NPLs to
gross advances has declined by at least 5 percentage points over last three years. The more
gratifying feature is that the ratio of net NPLs/net advances has declined from 15 percent to 11
percent as the banks and DFIs increased their holding of provisions to Rs 142 billion which
covered 56 percent of their classified portfolios of both foreign and domestic loans. The situation
will further improve in 2002 as the banks make more provisions against a declining portfolio
of NPLs.
2.2.2 PROACTIVE TREATMENT OF THE STOCK OF NPLs
The State Bank is still not satisfied with this declining trend of these NPLs, as the spread
between the deposit and lending rates is still high due to this drag. It has adopted a multi-pronged
approach to resolve this issue. First, it has put pressure on the banks and DFIs to accelerate
recovery. During the past three years, an amount of more than Rs 40 billion or 20 % of 1999
outstanding stock of NPLs has been recovered in cash. Second, it has decided to distinguish
between willful defaulters and circumstantial defaulters. The cases of willful defaulters have
been referred to NAB for action under the NAB Ordinance. NAB has helped in recovering
(including rescheduling) Rs 17.5 billion so far from these defaulters. Third, the Committee on
Revival of Sick Units (CRSU) has been authorized to restructure the NPLs and revive the
underlying sick units which are found to be financially and economically viable. Fourth, the
Government has created an asset resolution framework in the form of Corporate and Industrial
Restructuring Corporation (CIRC). This corporation acquires the bad loans from nationalized
banks at a discount and auctions them publicly thus taking away the assets from the existing
owners and repaying the proceeds to the banks. Fifth, as there are aged loans which can hardly
be recovered due to passage of time and the diminution in their value, the SBP has developed
general guidelines for the use of bank boards of Directors to write-off these loans particularly to
help small and medium borrowers. Finally, 80 percent of these non-performing loans are
concentrated in seven public owned banks and DFIs. NDFC has been merged with NBP. UBL
has been privatized and HBL is in the process of privatization. IDBP and ADBP are being
restructured and NBP shares are being floated. As this link between political loans and the
public-owned banks will be severed the probability of huge accretion of bad loans in the future
will be minimized. The banks are in the business of risk taking and there are occasions when
exogenous shocks or business cycles or frequent changes in government policies do turn some of
their assets sour. Until and unless there is no personal motive of the bankers or any political
pressure, the write off of loans and cleaning up of their balance sheets is the normal practice of
the banks all over the world and Pakistani banks should not hesitate to take appropriate action on
the basis of transparent criteria and policy guidelines.
The Board of Directors and the regulators should exercise oversight and make sure that the
decisions taken by the bank management conform to the approved criteria and guidelines. Only
willful defaulters should be taken to task and made to repay their full liabilities. Legal action
should be taken against them and their cases referred to NAB. (c) Stemming flow of new NPLs.
The banking sector reforms implemented since 1997 have improved the quality of assets. The
flow of NPLs has been significantly contained. The ratio of NPLs to total loans disbursed since
1997 has remained around 5 percent much lower than international norms. This will ensure that
the future ratios of NPLs will look much better than the historical or current ratios. It has also
been observed that the NPLs and their ratios to advances are much lower in case of domestic
private banks and foreign banks. As the nationalized commercial banks (NCBs) and DFIs are
privatized or liquidated or merged the incidence of new non-performing loans will be reduced
significantly. There has been a perceptible change in the credit culture of the NCBs who now pay
increasing attention to more rigorous credit appraisal, proper credit documentation, monitoring
and follow up.
Their management have also developed mechanisms for better risk management and
discontinued lending on political considerations. This new Credit Culture, of course, has some
negative repercussions as credit officers have become more risk averse in recommending new
loans and the potential borrowers have become more cautious in contracting new loans. Decline
in private sector credit can be partially attributed to this risk aversion among the bank credit
staff. The SBP is trying to mitigate this by asking the banks to diversify their portfolios and open
up new lines of business – consumer financing, mortgage financing, SME lending, microfinance,
agriculture credit and thus manage the aggregate risks better. This diversification and risk
management strategy should help meet the broad credit demand of the various segments of the
economy but also stem flow of nonperforming loans.
2.2.3 POLICY AND REGULATORY ENVIRONMENT
The State Bank’s policies and regulatory environment have also been revamped to resolve this
problem. Interest rates have declined significantly and low interest rates should help the
borrowers in repaying their stuck up loans. Information on exposure to various individual
companies and groups will now be available to the banks on-line to help them in making
informed decisions on credit extension. The protracted and cumbersome legal processes and
prolonged litigation for execution of decrees have been a major stumbling block in the recovery
of loans. An important recent development has been the enactment of a new recovery law in
2001 which enables the banks to repossess the collaterals without recourse to litigation. A new
bankruptcy law is also in offing which will permit orderly resolution of debtor obligations under
distress. The SBP has also strengthened its supervisory capacity by shifting to risk based
supervision and by assessing the strengths and weaknesses of internal controls, systems and risk
management mechanisms within the financial institutions. The supervisors make a more prudent
evaluation of the provisioning requirements and take corrective actions to enable the financial
institutions to set aside the right amounts of provisions. The introduction of market based
instruments, swap desks, development of a yield curve, allowing the banks to raise second tier
capital through subordinated debt and matching their asset and liability maturities are some of
the additional steps which have been taken by the SBP to facilitate the banks to manage their
risks in a more prudent manner. To sum up, while significant progress has been made in dealing
with the old stock of non-performing loans of the banks and DFIs, SBP is still not satisfied with
the existing situation. The good news is that the proportion of NPLs among the new loans
approved since 1997 is shrinking while a combination of policies aimed at cash recoveries,
rescheduling, restructuring, sale of assets to third parties, execution of legal decrees, write off of
aged and irrecoverable loans are being pursued to reduce the quantum of old stock. For a variety
of reasons legal, accounting, valuation, prudential and regulatory the stock may continue to
Show an upward rise as mark-up is added over time to overdue principal, exchange rate
revaluations are effected and more rigorous standards are applied by the SBP in classifying and
reporting these loans. This rise should, therefore, be seen in the correct and overall perspective,
as explained in this article.
Chapter # 03
RESEARCH METHODOLOGY & RESEARCH DESIGN
3.1 RESEARCH DESIGN
Research design is based on categories that include conceptual frame work and theoretical frame
work this include the diagram that will explain the relationship between the variables and
explains that what are the direct and moderate variable. As the word used moderated meant by
that those variables that only influence the direct variables or independent variables.
3.2 CONCEPTUAL FRAME WORK
3.2.1 Dependent variables:-
Banking industry profitability
3.2.2 Independent Variables:-
Increasing non-performing loans
Credit risk assessment policies
3.2.3 Moderate Variables:-
SBP regulations and restrictions
Sample size
Bank Al- Habib Ltd data will be consist of 12 years from 2000 to 2011
3.3 THEORETICAL FRAME WORK
Growing non-performing loans (NPLs) has become a threat for financial system of Pakistan
which should be taken as a challenge by monetary authorities. SBP lowered interest rates in the
last monetary policy may be due to this factor as economic growth is very slow which results in
low demand and hence higher non-performing loans. NPLs have been continuing to grow since
2008 and now they have toughed unprecedented level of Rs 594.5 billion which should be a
cause of concern for the economic managers trying to bring economy back on track, it said.
Growing NPLs are result of unheard-of level of government borrowings which has choked the
productive sector of the country due to crowding out effect , said Dr. Murtaza Mughal, President
Pak economy watch. SBP has indicated an alarming growth in Non-Performing loans in banks
and other financial institutions. Although the growth rate has remained low in the 2nd quarter
2010, yet it has reached to the level of 473 billion rupees. For the last two years, especially after
financial crises faced by the banks and financial institutions all over the world, the banks and
other financial institutions in Pakistan have also slowed down their loan / advances offering
services to the private sector. Due to bad economic situation and very low development activities
in Industrial sector, the banks and other institutions are either offering loans at very high interest
rates or investing in government owned securities. This increase in NPL level can rightly be
associated with the bad economic situation due to different problems like energy crises, load-
shedding and prices hike in raw materials and other utilities; faced by industrialists. This increase
in NPLs has not only created hesitation in banks in offering new loans but also urged them to
play safe thru’ investing in government securities. State Minister for Finance, Revenue,
Economic Affairs, Statistic and Planning and Development Hina Rabbani Khar January 2nd 2011
informed the National Assembly that Non-Performing Loans (NPLs) of banking sector has
increased from 10 percent to 14 percent during the last two years. Replying to a Call Attention
Notice raised by MNAs: Nuzhat Sadiq, Nisar Tanveer, Tahira Aurangzeb and others regarding
unprecedented rise in NPLs of banking sector to Rs.494 billion. She said that high risks, security
situation, energy shortage and overall economic recession are main reasons for NLPs increase.
She said that 80 percent of the country’s banking is being run by private sector and the
government has no role in writing off NPLs. The State Bank of Pakistan (SBP) being a regulator
only formulates rules for banking sector, she added. The minister, besides local banks foreign
banks waived off loans during the last years while private banks are being run by their Board of
Directors.
3.3.1 NON-PERFORMING LOANS IN THE BANKING INDUSTRY
3.3.1.1 INTRODUCTION
Financial institutions such as banks are expected to maintain their credit management due to the
increasing rate of non-performing loans. The increasing number of non-performing loans of
different entities and individual creates a significant impact and negative values to the financial
streams. In the long-run, this same impact will reach the entire economy and leads to increase the
credit crisis. In this paper, there is an interest drawn by the researcher/s regarding the reason or
several reasons that lead to non-performing loans. In the investigation, there will be appropriate
analysis that can generate the recommendation on remedies to lessen the rate of non-performing
loans. The focus of the paper is the situation in most of the developing countries, particularly in
Kenya.
3.3.1.2 FAILURE IN MANAGEMENT
Banks are absolutely strong to hold the financial crisis. But in the recent years, this characteristic
of bank changed due to the various economic changes and challenges offered by the
globalization. Added to this disadvantage is the growing numbers of non-performing loans that
affects the financial stream and operations of the bank. The main objective of the bank in
offering the financial credit and loans for the entities and small individuals can be viewed in the
noble mission “to lessen the poverty”. The procedures and operations of the banks are tested
through their model country. However, there are instances that the loan performance fails to
follow its original plan and fail to produce the expected outcome because of the two aspects – the
credit management of the financial institution and the failure of the individual or entities to
wisely use the loans.
3.3.1.3 FIRST REASON IS CREDIT MANAGEMENT
From the simple transaction, different problems may arise. The failure of the customer to
disclose any personal information during the application can be the greatest reason that might
influence the overall performance of the banks. The fact that the information is insufficient may
affect the loan’s fruitful expectations. This is also the representation of the bank’s lack of
capacity to investigate and build strong transactions, as well as the debt collection.
3.3.1.4 SECOND REASON IS INDIVIDUALS / ENTITIES CAPACITY
The purpose of loans is to support the financial needs of the customers according to their
proposed businesses or specific needs. If the bank doesn’t see any fruitful investment in the
proposed plan, they will only simply reject it. However, due to the personal greediness, the
individuals draw assumptions and deceive the banks. Where, on the other hand, the bank is
incapacitated to extend its investigation to ensure that all the information they receive are true
and legal.
3.3.1.5 RECOMMENDATIONS
The non-performing loans are great issues which includes the government and the economy.
However, there are still suggestions that can be adopted to reduce and prevent the growing
numbers of non-performing loans. First, the bank should include the two actions in their
operations (Harrison, 2006): (a) estimate the non-performing loans and allocate it to the
corresponding borrowers but consider how unpaid loans are recorded in the accounts in such a
way as to increment principal outstanding; and (b) estimate the interest received, rather than the
receivable, and on the interest payable so that the performing loans are not affected by the non-
performing loans.
Since the problem that constitutes in the non-performing loans is associated with the operation of
the banks, there should be an aggressive debt collection policy. The lack of aggressiveness is
popular in the developing countries and this is perceived as the banks specific factor that can also
contribute to the non performing debt problem (Waweru, & Kalani, 2009). Banks should
increase their competency and maintain it until they recover their position and have a normal
operation. Because of the pursuance in the economic development of the country, the economic
downturn in the internationals setting should be prevented to influence the domestic situation of
Kenya. Since the banks have no control over the economic uncertainties, then, they must allow
the government to have its action over the issues of inflation and monetary policies.
In a highly competitive environment, the capabilities of the managers to handle the pressures and
the increasing demand of the customers can be the problem that may arise in the workforce
(Blaauw, 2009). Therefore, training and developmental options should be available to prevent
the failure in assessing the capabilities of the individuals or entities to generate the interests in
their loans.
3.3.1.6 CONCLUSION
By polishing the credit policies and establishing a strong capacity in the management, the bank
can handle the non-performing loans, which can duly affect the progress of the economy. Thus,
the involvement of the appropriate and updated credit management should be prioritized
3.4 RESEARCH METHODOLOGY
For a simple commercial bank balance sheet, assets are mainly composed of commercial loans
and other earning assets; while on the liability side, deposits and capital are the main
components. Thus, we can conjecture that the loan growth is affect by deposit growth, capital
growth and other earning assets growth. In addition, we take the non-performing loan growth
into consideration. The basic model is as follows:
1,4,3,2,10, −++++= tititititi NPLGRaOEAGRaCGRaDGRaaLGR (1)
Where the index i is the index for individual banks and t is the index for time period. tiLGR , is
the loan growth rate, tiDGR , is the deposit growth rate in each time period t , tiCGR , is the
capital growth rate, tiOEAGR , is the other assets growth rate, and 1, −tiNPLGR is non-performing
loan growth rate of the previous year.
As financial intermediations, commercial banks’ main function is to receive deposits and make
loans to facilitate the flow of capitals. For most of the commercial banks, deposits are the main
funding sources for commercial banks’ assets. And loans take up the biggest proportion in the
asset portfolio. With the expansion of the asset size, banks will expand the volume of the loans to
re-balance the asset portfolio. Under the normal situation, loan growth rate is expected to move
in the same direction as the growth of deposits. The sign in front of tiDGR , , thus, is expected to
be positive.
Chapter # 04
STATISTICAL REPORTING & DATA ANALYSIS
Table 4.1
Bank AL Habib Ltd Data from the year 2000 to 2011 Rs in Millions
Year Gross
Advances Deposits Profit
Before Tax Profit
After Tax NPLs % of NPLs to Advances
2000 14,772 17,822 403 373 117 0.79%
2001 16,004 24,697 551 246 185 1.16%
2002 23,994 34,240 619 290 482 2.01%
2003 35,544 46,178 1,513 1,012 646 1.82%
2004 47,537 62,171 1,039 541 206 0.43%
2005 55,526 75,796 2,022 1,464 383 0.69%
2006 71,036 91,420 2,689 1,761 388 0.55%
2007 79,447 114,819 3,052 2,211 217 0.27%
2008 101,402 144,390 3,579 2,425 863 0.85%
2009 108,373 189,280 4,512 2,856 2,068 1.91%
2010 129,083 249,774 5,656 3,602 2,944 2.28%
2011 120,003 302,098 7,155 4,533 3,204 2.67%
(Source: Bank AL Habib Ltd Annual Reports)
4.1 FINDINGS AND INTERPRETATION OF THE RESULTS
1,4,3,2,10, −++++= tititititi NPLGRaOEAGRaCGRaDGRaaLGR
Where the index i is the index for individual banks and t is the index for time period. tiLGR , is
the loan growth rate, tiDGR , is the deposit growth rate in each time period t , tiCGR , is the
capital growth rate, tiOEAGR , is the other assets growth rate, and 1, −tiNPLGR is non-performing
loan growth rate of the previous year.
REGRESSION ANALYSIS:
TABLE 4.2:
Model Summary ANOVA
Model R Square Adjusted R Square Model F Sig.
1 .851 .796 Regression 24.097 .000
Residual
Total
(Source: From this study)
INTERPRETATION:
This table shows the fitness of model. R Square of 0.851 indicates that the model is 85.1% fit for
the analysis. This model shows that the independent variables have explained almost 85.1
percent of the influencing factors that affect the profitability of Banks. However, this also
indicates that there is 20.4 percent unexplained variation, which shows that there are some
significant variables missing in the model. This may call for a further study in the same area.
This table also indicates that the regression is highly significant at 0.05 level of significance as
the Sig. value of regression is less than 0.05. This defines that there is a positive impact of log of
growth rate (NPL’s), tiDGR , is the deposit growth rate in each time period, tiCGR , is the capital
growth rate, tiOEAGR , is the other assets growth rate has a significant relationship with LGR loan
growth rate. F statistics value of 24.097 significant at 0.000 suggests that the model calculation
of R square is correct.
Table 4.3:
(Source: From this study)
INTERPRETATION:
ANALYSIS OF T-STATS
The statistical analytical report suggest that deposit growth rate has a significant relationship
with loan growth rate as it reflects the value of 0.523 which stand with in non-critical region as
well as capital growth rate has a significant relationship with the variable LGR as it reflects the
value of t-stats of -1.284 which stands between the range of critical region. Other asset growth
rate provides the t-stats of -0.836 which is still in the non-critical region and NPL growth rate
Coefficients
Unstandardized Coefficients
Standardized Coefficients
Collinearity Statistics
Model
B Std. Error Beta t Sig. Tolerance VIF
(Constant) LGR .017 .010 1.689 .113
DGR .000 .000 .0228 .523 .609 .045 9.306
CGR -.001 .000 -.0627 -1.284 .001 .569 1.498
OEAGR -.011 .013 -.0107 -.836 .417 .231 1.146
NPLGR .004 .002 .0340 1.867 .083 .431 2.318
1
Loan Loss Provision/Total Loans
-.300 .128 -.391 -2.346 .034 .379 15.947
provides result of 1.867 which is still in the non-critical region therefore all the variables show
the rejected values that means they have relationship with each other.
ANALYSIS OF BETA
Deposit growth rate analysis (DGR) represents the value of 0.0228 that mean that if loan growth
rate expends by 1% then deposit rate could be expected to gain by 2.28% whereas same is the
situation with the capital growth rate (CGR) if loan growth rate enhance by 1% then CGR could
be reduced by 6.27%. if loan growth rate increases by 1% then it would be possible that
OEAGR will decline by 1.07% where as if NPL’s i.e. non-performing loans will increase by
3.4% if loan growth rate will increase by 1%.
ANALYSIS OF MULTI-CO-LINEARITY
In some cases, multiple regression results may seem paradoxical. Even though the overall P
value is very low, all of the individual P values are high. This means that the model fits the data
well, even though none of the X variables has a statistically significant impact on predicting Y.
How is this possible? When two X variables are highly correlated, they both convey essentially
the same information. In this case, neither may contribute significantly to the model after the
other one is included. But together they contribute a lot. If you removed both variables from the
model, the fit would be much worse. So the overall model fits the data well, but neither X
variable makes a significant contribution when it is added to your model last. When this happens,
the X variables are collinear and the results show multicollinearity. To help you assess
multicollinearity, In Stat tells you how well each independent (X) variable is predicted from the
other X variables. The results are shown both as an individual R2 value (distinct from the overall
R2 of the model) and a Variance Inflation Factor (VIF). When those R2 and VIF values are high
for any of the X variables, your fit is affected by multicollinearity. The best solution is to
understand the cause of multicollinearity and remove it. Multicollinearity occurs because two (or
more) variables are related – they measure essentially the same thing. If one of the variables
doesn’t seem logically essential to your model, removing it may reduce or eliminate
multicollinearity. Or perhaps you can find a way to combine the variables. For example, if height
and weight are collinear independent variables, perhaps it would make scientific sense to remove
height and weight from the model, and use surface area (calculated from height and weight)
instead. You can also reduce the impact of multicollinearity. One way to reduce the impact of
collinearity is to increase sample size. You'll get narrower confidence intervals, despite
multicollinearity, with more data. Even better, collect samples over a wider range of some of the
X variables. If you include an interaction term (the product of two independent variables), you
can also reduce multicollinearity by "centering" the variables. To do this, compute the mean of
each independent variable, and then replace each value with the difference between it and the
mean. In our research model we are viewing that the value of tolerance level is less than 0.5 and
the value of VIF is also less than 10 degree that suggest that the model is not affected by the
problem of multi-co-linearity.
4.2 HYPOTHESIS ASSESSMENT SUMMARY
Table 4.4
S.No Hypothesis t Sig Result 1 Hypothesis 1:
There is no significant relationship with loan growth rate and deposit growth rate.
2.271
.029
Rejected
2 Hypothesis 2: There is no significant relationship between loan growth rate and capital growth rate
-.915
.366
Accepted
3 Hypothesis 3: There is no positive relationship other asset growth rate and loan growth rate
6.059
.000
Rejected
4 Hypothesis 4: There is no positive relationship between bank profitability and inflation rate
7.635
.000
Accepted
5 Hypothesis 5: There is no positive relationship between loan growth rate and NPL’s
.547
.588
Rejected
(Source: From this study)
FORMULA USED IN RESEARCH ANALYSIS
T-STATISTICS
F-STATISTICS
BANK AL-HABIB VARIOUS GRAPHS
GRAPH # 1
(Source: Bank AL Habib Annual Reports ‐ For details see page number 87)
GRAPH # 2
(Source: Bank AL Habib Annual Reports ‐ For details see page number 87)
GRAPH # 3
(Source: Bank AL Habib Annual Reports ‐ For details see page number 87)
GRAPH # 4
(Source: Bank AL Habib Annual Reports ‐ For details see page number 87)
GRAPH # 5
(Source: Bank AL Habib Annual Reports ‐ For details see page number 87)
GRAPH # 6
(Source: Bank AL Habib Annual Reports ‐ For details see page number 87)
GRAPH # 7
(Source: Bank AL Habib Annual Reports ‐ For details see page number 87)
GRAPH # 8
(Source: Bank AL Habib Annual Reports ‐ For details see page number 87)
Chapter # 05
CONCLUSION AND RECOMMENDATION
5.1 CONCLUSION
This research approach provided us the role of NPL’s on banks profitability and credit risk
assessment moreover it explains that how loan growth rate is a basic driver for the non-
performing loans. Statistical evaluation was based on the data charts of Bank AL-Habib Ltd
which explains the rising trends in non-performing loans as per the diagrammed view mentioned
above that explains that deposit rate have gain the high growth which explains the good capital
adequacy ratio but still the profit after tax is showing the low trend value which means that there
are less number of loans that are provided to the borrowers due to SBP strict regulations as well
as it predicts that due to market risk Bank Al-Habib can suffer losses in future as it have high
deposit rate but it can be predicted that financer behavior is converting towards bank deposits
rather than to take a risk and enjoy good profit. Moreover it can be predicted based on BAHL
data that most of the banking industry will suffer losses due to market risk situation and changing
behavior of financers.
5.2 RECOMMENDATIONS
Based on our findings, it means that commercial banks should pay attention to several factors
when providing loans in order to curtail the level of impaired loans. Specifically, commercial
banks need to consider the international competitiveness of the domestic economy since this may
impair the ability of borrowers from the key export oriented sectors to repay their loans which in
turn would result in higher non-performing loans. These lending institutions should also take the
performance of the real economy into account when extending loans given the reality that loan
delinquencies are likely to be higher during periods of economic downturn. Finally, banks should
constantly review the interest rates on loans since loan delinquencies are higher for banks which
increase their real interest rates.
5.3 FUTURE RESEARCH
Since our results for NPL’s are encouraging, the authors will replicate this study for with
different banks in the Caribbean. In order to extend the literature on non-performing loans, the
authors plan to incorporate corporate governance and the regulatory environment in our future
research. This decision is motivated by two primary reasons: (i) the financial crisis in the
Pakistan was blamed on deregulation and the weak regulatory framework in this country; and (ii)
ethics seem to at the heart of the financial crisis in the Pakistan. While there are reasons to
suspect that other financial crises occurred because of these factors, they were ignored in
previous studies.
5.4 LIMITATIONS
This study has been conducted by a sample of one bank that is Bank Al Habib it will not
reflects the actual picture of all banks.
Some important variables are missing which means the model is incomplete.
Due to time constraints researcher was unable to find a macro-economic impact of NPL’s
This research has been performed on data i.e. from 2001 to 2011 it could be also viewed
further by adding the data of 2012
5.5 SOME IMPORTANT LITERATURE, DISCUSSION & OBSERVATIONS
Examining non-performing loans has expanded in line with the interest afforded to
understanding the factors responsible for financial vulnerability. This situation may be attributed
to the fact that impaired assets plays a critical role in financial vulnerability as evidenced by the
strong association between NPLs and banking/financial crises in Argentina, East Asia and Sub-
Saharan African Countries during the 1990s. In this section we review the existing literature so
as to formulate a theoretical framework to investigate the determinants of non-performing loans
in Guyana. Keeton and Morris (1987) present one of the earliest studies to examine the causes of
loan losses. In the latter paper the authors examined the losses by 2,470 insured commercial
banks in the United States (US) over the 1979-85. Using NPLs net of charge-offs as the primary
measure of loan losses Keeton and Morris (1987) shows that local economic conditions along
with the poor performance of certain sectors explain the variation in loan losses recorded by the
banks. The study also reports that commercial banks with greater risk appetite tend to record
higher losses.
Several studies which followed the publication of Keeton and Morris (1987) have since proposed
similar and other explanations for problem loans in the US. Sinkey and Greenwalt (1991), for
instance, investigate the loan loss-experience of large commercial banks in the US; they argue
that both internal and external factors explain the loan-loss rate (defined as net loan charge offs
plus NPLs divided by total loans plus net charge-offs) of these banks. These authors find a
significant positive relationship between the loan-loss rate and internal factors such as high
interest rates, excessive lending, and volatile funds. Similar to the previous study, Sinkey and
Green walt (1991) report that depressed regional economic conditions also explain the loss-rate
of the commercial banks. The study employs a simple log-linear regression model and data of
large commercial banks in the United States from 1984 to 1987.
Keeton (1999) uses data from 1982 to 1996 and a vector auto regression model to analyse the
impact of credit growth and loan delinquencies in the US. It reports evidence of a strong
relationship between credit growth and impaired assets. Specifically, Keeton (1999) shows that
rapid credit growth, which was associated with lower credit standards, contributed to higher loan
losses in certain states in the US. In this study loan delinquency was defined as loans which are
overdue for more than 90 days or does not accrue interest.
Studies that examined other financial systems also provide similar results to those in the US. For
instance, Bercoff et al (2002) examine the fragility of the Argentinean Banking system over the
1993-1996 period; they argue that NPLs are affected by both bank specific factors and
macroeconomic factors. To separate the impact of bank specific and macroeconomic factors, the
authors employ survival analysis.
Using a dynamic model and a panel dataset covering the period 1985-1997 to investigate the
determinants of problem loans of Spanish commercial and saving banks, Salas and Saurina
(2002) reveal that real growth in GDP, rapid credit expansion, bank size, capital ratio and market
power explain variation in NPLs. Furthermore, Jimenez and Saurina (2005) examine the
Spanish banking sector from 1984 to 2003; they provide evidence that NPLs are determined by
GDP growth, high real interest rates and lenient credit terms. This study attributes the latter to
disaster myopia, herd behaviour and agency problems that may entice bank managers to lend
excessively during boom periods.
Meanwhile, Rajan and Dhal (2003) utilise panel regression analysis to report that favourable
macroeconomic conditions (measured by GDP growth) and financial factors such as maturity,
cost and terms of credit, banks size, and credit orientation impact significantly on the NPLs of
commercial banks in India.
Using a pseudo panel-based model for several Sub-Saharan African countries, Fofack (2005)
finds evidence that economic growth, real exchange rate appreciation, the real interest rate, net
interest margins, and inter-bank loans are significant determinants of NPLs in these countries.
The author attributes the strong association between the macroeconomic factors and non-
performing loans to the undiversified nature of some African economies.
More recently Hu et al (2006) analyse the relationship between NPLs and ownership structure of
commercial banks in Taiwan with a panel dataset covering the period 1996-1999. The study
shows that banks with higher government ownership recorded lower non-performing loans. Hu
et al (2006) also show that bank size is negatively related to NPLs while diversification may not
be a determinant.
ECONOMETRIC MODEL AND ESTIMATION PROCEDURE
Based on our review of the literature it is clear that there is extensive international evidence
which suggests that NPLs may be explained by both macroeconomic and bank specific factors.
In this study we employ a reduced form econometric model that is similar to Jimenez and
Saurina (2005) to ascertain the determinants of NPLs in the Guyanese banking sector. The model
is a simple linear regression function that links the ratio of NPLs to total loans and key
macroeconomic and bank specific variables. The general regression equation is of the form:
lnNPL_Ai,t = β0i + β1lnNPL_Ai,t-1 + β2lnL_Ai,t + β3SIZEi,t + β4∆LOANSi,t +β5∆LOANSi,,t-1 + β6∆LOANSi,,t-2
+ β7lnRIRt + β8lnRIRt-1 + β9lnINFt + β10lnINFt-1 + β11∆GDPt + β12∆GDPt-1+ β13lnREERt + β14lnREERt-1
+ η + εi,t
i = 1,…N, t= 1,…T
where: lnNPLi,t and lnNPLi,t represent the natural log of the ratio of NPLs to total loans for bank
i in year t and t-1; ∆GDPt and ∆GDPt-1 represent the annual growth in real GDP at time t and t-1
respectively; lnRIRt and lnRIRt-1 denote the natural log of the real interest rates (measured as the
difference between the weighted average lending rate and the annual inflation rate) at time t and
t-1; lnREERt and lnREERt-1 indicates the natural log of the real effective exchange rate at time t
and t-1; lnINFt and lnINFt-1 indicate the natural log of the annual inflation rate at time t and t-1;
SIZEi,t is the ratio of the relative market share of each bank’s assets that capture the size of the
institution at time t; lnL Ai,t is the natural log of the loans to total asset ratio for bank i in year t;
∆LOANSi,t, ∆LOANSi,,t-1 and ∆LOANSi,,t-2 represent the growth in loans for bank i in year t, t-
1,and t-2 respectively; and εi,t is the white noise error term. In the model, the coefficient β0i
captures the idiosyncratic behaviour of commercial banks. The fixed effect coefficient allows for
detecting those factors affecting NPLs that do not change over time.
The model is estimated using pooled least squares with a fixed effect estimator. Researchers
who utilise this estimation technique argue that it is more efficient than the ordinary pooled least
squares since it accounts for heterogeneity that is often present in panel datasets.1 In order to
minimise the effect of heteroskedasticity, the White robust standard errors are computed. We
also estimate our model with the dependent variable on the right-hand side with a lag of one
year. This is done to overcome the persistence exhibited by the ratio of NPLs to total loans over
our sample period (Figure 1). Additionally, we follow the general to specific approach to arrive
at the parsimonious model. The rationale for considering each variable is provided in the ensuing
section of the paper.
1 See Hu et al. (2006) and Wooldridge (2009).
MOTIVATION AND DESCRIPTION OF VARIABLES
Macroeconomic variables
The existing literature provides evidence that suggests a strong association between NPLs and
several macroeconomic factors. Several macroeconomic factors which the literature proposes as
important determinants are: annual growth in GDP, credit growth, real interest rates, the annual
inflation rate, real effective exchange rate (REER), annual unemployment rate, broad money
supply (M2) and GDP per capital etc. This study only considers the growth in real GDP (ΔGDP),
annual inflation (INF) and the real effective exchange rate (REER).
There is significant empirical evidence of a negative relationship between the growth in real
GDP and NPLs (Salas and Suarina, 2002; Rajan & Dhal, 2003; Fofack, 2005; and Jimenez and
Saurina, 2005). The explanation provided by the literature for this relationship is that strong
positive growth in real GDP usually translates into more income which improves the debt
servicing capacity of borrower which in turn contributes to lower non-performing loans.
Conversely, when there is a slowdown in the economy (low or negative GDP growth) the level
of NPLs should increase.
The literature also provides evidence of a positive relationship between the inflation rate and
non-performing loans. Fofack (2005), for instance, shows that inflationary pressures contribute
to the high level of impaired loans in a number of Sub-Saharan African countries with flexible
exchange rate regimes. According to this author, inflation is responsible for the rapid erosion of
commercial banks’ equity and consequently higher credit risk in the banking sectors of these
African countries.
There is also evidence in the literature of a positive association between NPLs and real effective
exchange rate. Fofack (2005) reveals that changes in the real effective exchange rate have a
positive impact on NPLs of commercial banks that operate in some Sub-Saharan African
countries with fixed exchange rate regimes. The author argues that this result is due to the large
concentration of loans to the export-oriented agriculture sector, which was adversely affected by
the appreciation in the currency of these countries during the 80s and early 90s.
The macroeconomic variables are included in our econometric model both contemporaneously
and with one year lag since adverse shocks from the economy may not impact immediately on
the loan portfolios of banks. Except for ΔGDP, the natural logarithms of the macro variables are
used to estimate our model. We were unable to take the log of GDP since there were negative
growth rates during our sample period. Additionally, while we allow the macroeconomic
variables to vary over time they are the same across institutions.
BANK SPECIFIC VARIABLES
Apart from macroeconomic variables, there is abundant empirical evidence that suggests that
several bank specific factors (such as, size of the institution, profit margins, efficiency, the terms
of credit (size, maturity and interest rate), risk profile of banks (measured by several proxies
including total capital to asset ratio and loans to asset ratio) are important determinants of NPLs.
This study only considers four bank specific variables owing to data availability. These are: real
interest rate (RIR), bank size (SIZE), annual growth in loans (∆LOAN) and the ratio of loans to
total asset (L_A).
The impact of real interest rates on NPLs is extensively documented in the literature. In fact,
several studies report that high real interest rate is positively related to this variable (see for
example, Jimenez and Saurina, 2005 and Fofack, 2005). We construct this variable by
subtracting the annual inflation rate from the weighted average lending rate of each bank. The
variable is included contemporaneously (RIRi,t) and with a lag of one year (RIRi,t-1).
Excessive lending by commercial banks is often identified as an important determinant of NPLs
(Salas and Saurina, 2002; and Jimenez and Saurina, 2005; Keeton and Morris, 1987; and Sinkey
and Greenwalt, 1991; and Keeton, 1999). The variable to capture credit growth is constructed by
finding the annual percentage change in the loan portfolio for each bank (ΔLOANS). This
variable is introduced into our model contemporaneously and with up to two lags. Like the
growth in real GDP, we were unable to take the natural logarithm of (ΔLOANS) since there were
periods when some commercial banks provided lower credit to the private sector. We expect this
variable to have a significant positive relationship with NPLs since the literature shows that rapid
credit growth is often associated with higher NPLs.
The empirical evidence relating to the impact of bank size on NPLs appears to be mixed. For
instance, some studies report a negative association between NPLs and bank size (see Rajan and
Dhal, 2003; Salas and Saurina, 2002; Hu et al, 2006). According to these studies, the inverse
relationship means that large banks have better risk management strategies that usually translate
into more superior loan portfolios vis-à-vis their smaller counterparts. There are also studies
which provide evidence of a positive association between NPLs and bank size (see Rajan and
Dhal, 2003). In this study the SIZE variable is constructed by computing the relative market
share of the asset of each commercial bank.
There is also evidence in the literature that shows a strong positive relationship between NPLs
and the ratio of loans to asset (L_A), which captures the risk appetite of banks (see Sinkey and
Greenwalt, 1991). The supporting rationale is that banks that value profitability more than the
cost of higher risk (represented by a high loan to asset ratio) are likely to incur higher levels of
NPLs during periods of economic downturn. In this paper, SIZE and L_A variables are included
contemporaneously. In addition, our bank specific variables vary with time and across
institutions.
In this study we use a panel dataset that consists of firm-level data for six commercial banks that
operated during the 1994 to 2004 period.2 The dataset also includes macroeconomic variables
such as the annual inflation rate, real effective exchange rate (REER), and annual growth in real
GDP over the period of analysis. The firm-level data were obtained from the Annual Reports of
Commercial Banks while the macroeconomic variables were obtained form the Bank of Guyana
Annual Reports and the International Financial Statistics (IFS).
The time period covered by our panel is selected for two primary reasons. Firstly, data for NPLs
before 1994 were not available. Secondly, several local commercial banks (in collaboration with
the government) embarked on an exercise to restructure their NPLs to the rice producing sector
during 2005. This initiative saw a sharp contraction in the impaired assets of the banks and can
therefore distort the econometric analysis.
STYLIZED FACTS
2 The data for GNCB are excluded from the panel. In 2002 GNCB was merged with GUYBANK, where the former took over the non‐performing loans of the latter. The merger of these financial institutions therefore resulted in a significant growth in the aggregate NPLs of the banking sector which was not related to the performance of the domestic economy or credit policy of GNCB. Since the aim of this exercise is to determine the relationship between the growth in non‐performing loans and key macro‐economic and bank specific variables, GNCB was excluded to avoid the distortion that may be caused by the inclusion of this financial institution in our analysis.
The adoption of the Economic Recovery Programme (ERP) in 1989 saw the liberalisation of the
Guyanese banking sector and the expansion of the real economy which in turn encouraged many
commercial banks to extend credit rapidly to the private sector. The average real GDP growth
rate during the 1991-97 was 7.1 percent. The ratio of credit to GDP rose from 19.8 percent in
1991 to approximately 50 percent in 1997. However, these trends were reversed after 1997 due
to political instability, a slowdown in the real economy, and unfavourable external circumstances
which contributed to a sharp increase in NPLs. The total NPLs of the banking system which
amounted to G$786 million in 1994 expanded to reach G$21 billion (or 45 percent of total loans
and advances) at end-2001. The strong co-movement between the ratio of NPLs/total loans and
our macroeconomic variables (ΔGDP and REER) depicted in Figure 1 clearly reflects the
sensitivity of impaired loans to the real economy and adverse external shocks.
The widespread default on loans during the mid-1990s several commercial bank adopted a
cautious lending stance. As a consequence, credit growth slowed significantly after 1998 as
commercial banks shifted to safer investments, mainly treasury bills. These trends in the lending
policies of local commercial banks are reflected in Figure 2, which shows a contraction in credit
growth and the ratio of loans to total assets. Based on Figure 2, credit growth reduced continually
from 47 percent during 1995 to below 1 percent in 2004. The ratio of loans to asset also reduced
from 45 percent to 31 percent over the corresponding period. The strong co-movements between
NPLs and the various bank specific and macroeconomic factors are not only clearly visible from
the Figures above but are confirmed by our correlation analysis reported in Table 2, which
presents the correlation coefficient between NPLs/total loan ratio and the bank specific and
macroeconomic variables for our panel dataset from 1994 to 2004.
In this study we employ a fixed effect panel model to identify the determinants of NPLs of local
commercial banks. Tables 3 and 4 summarize the results of our regression model which is
estimated using pooled least squares with a fixed effect estimator. Our model is estimated with a
balanced panel dataset that consists of both macroeconomic and firm level data from 1994 to
2004.
The variable L_Ai,t which represents the risk appetite of the commercial banks is positive and
significant at the 15 percent and 5 percent levels of significance in our general and parsimonious
models (see Tables 3 and 4). This means that banks which are high risk takers are likely to incur
greater levels of NPLs(see Sinkey and Greenwalt, 1987).
The variable SIZEi,t (which represents the size of the bank) is positive but insignificant. This
evidence which is inconsistent with previous studies (Rajan and Dhal, 2003; Salas and Saurina,
2002 and Hu et al, 2006) can be interpreted to mean that large banks are not necessarily more
effective in screening loan customers when compared to their smaller counterparts.
Similar to previous studies, however, we find a significant positive contemporaneous association
between the real interest rate variable (RIRi,t) and NPLs (see Sinkey and Greenwalt, 1991;
Fofack, 2005; Jimenez and Saurina, 2005). This indicates that when a commercial bank increases
its real interest rates this may translate immediately into higher non-performing loans.
The variable which captures the relative credit growth of commercial banks (∆LOANSi,t) is
negative and significantly related to NPLsat time t, t-1 and t-2 respectively. Based on our results,
it therefore follows that commercial banks which extend relatively higher levels of credit are
likely to incur lower non-performing loans. It is important to note that our results are contrary to
the international evidence which suggest a positive relationship between credit growth and NPLs
(see Salas and Saurina, 2002 and Jimenez and Saurina, 2005).
Based on Tables 3 and 4, the real effective exchange rate (REERt) is positively and significantly
related to NPLs suggesting that the international competitiveness of the domestic economy is an
important determinant of credit risk. In other words, whenever there is the deterioration in the
competitiveness in the local economy the level of NPLs emanating from the key export oriented
economic sectors is likely to increase. This evidence is not only consistent in 2004 but in 2005
but highlights the high levels of NPLs that were reported for the agriculture sector as a result of
lower commodity prices in the late 1990s.
Consistent with previous studies we also find a significant negative contemporaneous
relationship between ∆GDPt and NPLs (see Salas and Suarina, 2002; Jajan and Dhal, 2003;
Fofack, 2005; and Jimenez and Saurina, 2005). Similar to these studies we interpret our findings
to mean that an improvement in the real economy is likely to see an instantaneous reduction in
the non-performing loan portfolios of commercial banks.
Our results suggest a mixed relationship between inflation and non-performing loans. The
variable has a negative relationship with NPLs at time t but a positive impact at time t-1. This
means that high inflation in the current period should see a reduction in the level of NPLsin the
banking sector. However, high inflation from the previous period causes commercial banks to
incur higher non-performing loans. Apart from the mixed effects that inflation appears to exert
on NPLsthe coefficients of the inflation variables are not statistically significant in our regression
model.
REFERENCED BY EMPIRICAL STUDIES
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Appendix-1
Bank AL Habib Ltd Data from the year 2000 to 2011 Rs in Millions
Year Gross
Advances Deposits Profit
Before Tax Profit
After Tax NPLs % of NPLs to Advances
2000 14,772 17,822 403 373 117 0.79%
2001 16,004 24,697 551 246 185 1.16%
2002 23,994 34,240 619 290 482 2.01%
2003 35,544 46,178 1,513 1,012 646 1.82%
2004 47,537 62,171 1,039 541 206 0.43%
2005 55,526 75,796 2,022 1,464 383 0.69%
2006 71,036 91,420 2,689 1,761 388 0.55%
2007 79,447 114,819 3,052 2,211 217 0.27%
2008 101,402 144,390 3,579 2,425 863 0.85%
2009 108,373 189,280 4,512 2,856 2,068 1.91%
2010 129,083 249,774 5,656 3,602 2,944 2.28%
2011 120,003 302,098 7,155 4,533 3,204 2.67%
(Source: Bank AL Habib Ltd Annual Reports - Details mentioned below)
Appendix-2
(Source : Bank AL Habib Annual Report)
Appendix-3
(Source : Bank AL Habib Annual Report)
Appendix-4
(Source : Bank AL Habib Annual Report)
Appendix-5
(Source : Bank AL Habib Annual Report)
Appendix-6
(Source : Bank AL Habib Annual Report)
Appendix-7
(Source : Bank AL Habib Annual Report)
Appendix-8
(Source : Bank AL Habib Annual Report)
Appendix-9
(Source : Bank AL Habib Annual Report)
Appendix-10
(Source : Bank AL Habib Annual Report)
Appendix-11
(Source : Bank AL Habib Annual Report)
Appendix-12
(Source : Bank AL Habib Annual Report)
Appendix-13
(Source : Bank AL Habib Annual Report)