The association of Credit Risk, Liquidity Risk and Macroeconomic

Factors with the Financial Performance of Commercial Banks in

Pakistan

Muhammad Saad (02-112202-037)

Zarrar Khan (02-112211-035)
Umme Salma (02-112211-015)

A Project submitted to the Department of Management Studies,

Bahria University – Karachi Campus, in partial fulfillment of the
requirement for the BS (A&F) Degree

BS (Accounting & Finance)

Fall-2024
Bahria University, Karachi Campus
 1

ACKNOWLEDGEMENT

First, we are thankful to Allah, who is the holder of our breaths, without his orders
nothing is possible. In completing our project, we took help and guidelines of some
respected people, who deserve our appreciation and we are thankful to them. We
would like to show our deepest gratitude to Lecturer Dr Samina Raiz, Course
Supervisor, Bahria University for giving us helpful guidelines for this project through
numerous consultations.

We are thankful to all those who have directly and indirectly provided us with
guidance in completing this report. Our project Co-Ordinator Ma’am Fazeelat
Masood as well as our Dean and our H.O.D who gave us this golden opportunity to
do this project on the topic “The association of Credit Risk, Liquidity Risk and
Macroeconomic Factors with the Financial Performance of Commercial Banks
in Pakistan”.

This has also helped us with the extensive research we undertook due to which we
explored many more new things which will be helpful in our practical and work life.
We would also like to thank our classmates who gave valuable comments and
suggestions on this proposal which inspired us to improve our report. We would also
like to thank our parents who helped and supported us during these hard times and
motivated us a lot in finalizing this project within the limited time frame. We thank all
the people for their direct and indirect help due to which we were able to complete our
report.

PROJECT APPROVAL STATEMENT

APPROVAL FOR EXAMINATION

Candidate’s Name: Muhammad Saad Registration No.:69883

 2

TABLE OF CONTENTS

ACKNOWLEDGEMENT

LIST OF TABLES

LIST OF FIGURES

Figure 1 Conceptual Framework

Figure 2 Graphical Representation of Variables

Figure 3 Graphical Representation of Variables

Figure 4 Graphical Representation of Variables

Figure 5 Graphical Representation of Variables

Figure 6 Graphical Representation of Variables

 3

Figure 7 Graphical Representation of Variables

Figure 8 Graphical Representation of Variables

Figure 9 Graphical Representation of Variables

Figure 10 Graphical Representation of Variables

ABSTRACT

CHAPTER 1
1.2. Introduction 1
1.2. Background 1
1.3. Variables Description 2
Credit Risk 2
Liquidity Risk 3
Economic growth and Stability 3
Financial Performance (FP) 4
1.4. Problem Statement 5
1.5. Research Objectives: 5
1.6. Research Questions 6
1.6. Scope of the Study 6

CHAPTER NO 2

LITERATURE REVIEW
2.1. Theoretical Study 8
Agency Theory 8
Liquidity Return Trade-off Theory 8
Inflation & Profitability Theory 9
2.2. Empirical Study 9
2.3.Research Hypothesis 12
2.4.Conceptual Framework 13
 4

CHAPTER NO 3

RESEARCH METHODOLOGY
3.1. Research Approach 14
3.2. Research Design 14
3.3. Research Population 14
3.4. Measurement of Variables 15
3.5.Sample Size 15
3.6.Data Analysis 16

CHAPTER NO 4

DATA ANALYSIS AND DISCUSSIONS

4.1.Descriptive Analysis 18
4.2.Correlation Statistics 19
4.3.Graphical or Visual Representation of Variables Data 20

CHAPTER NO 5

CONCLUSION AND RECOMMENDATIONS

5.1. Conclusion 30
5.2. Limitations and Recommendations 31

REFERENCES
 5

LIST OF TABLES

Table 1 Measurement of Variables

Table 2 Descriptive Statistics
Table 3 Correlation Statistics
 6

LIST OF FIGURES

Figure 1 Conceptual Framework

Figure 2 Graphical Representation of Variables

Figure 3 Graphical Representation of Variables

Figure 4 Graphical Representation of Variables

Figure 5 Graphical Representation of Variables

Figure 6 Graphical Representation of Variables

Figure 7 Graphical Representation of Variables

Figure 8 Graphical Representation of Variables

Figure 9 Graphical Representation of Variables

Figure 10 Graphical Representation of Variables

 7

ABSTRACT

This study investigates the relationship between credit risk, liquidity risk,
macroeconomic factors and the financial performance of commercial banks in
Pakistan. Since the banking sector plays a vital role in the country's economic
development, understanding the dynamics of these risks is essential to ensure stability
and profitability. The study uses quantitative analysis to assess how fluctuations in
credit risk, liquidity risk and key macroeconomic indicators such as GDP growth,
inflation and interest rates affect banks’ financial performance. The results show that
higher credit risk and liquidity problems have a negative relation on profitability and
operational efficiency. Furthermore, macroeconomic factors significantly shape the
banking environment, influencing both risk profile and financial performance. The
study concludes with recommendations for improving credit and liquidity risk
management policies and strategic planning practices in commercial banks to mitigate
the negative factors and promote sustainable growth in an unstable economic
environment.
 1

CHAPTER 1
INTRODUCTION

1.2. Introduction
Commercial banks have a huge influence on the economic development by
adequately funding economic processes and, in turn, assist in maintaining the stability
of the financial systems of the countries since they are those institutions that can
absorb economic shocks as well as the most efficient in allocating the available
savings and funds to those areas where there are liquidity gaps and the demand for
these savings are met through credit activity (Bessis, 2019), It is one of the income
sources of banks, but on the other hand credit activity involves many risks facing the
lender and borrower such as liquidity risk, Market risk, credit risk, capital risk,
interest rate risk, exchange rate risk, political risk, and other types of risks. Yet, the
upcoming research will center on credit risk as being among the most significant. A
bank's financial success is contingent upon a multitude of risk factors, including
interest rate, credit, and liquidity concerns. Any organization's financial success is
susceptible to risk. The profitability and asset quality are negatively impacted by
inadequate credit risk management. It can result in a hike in non-performing loans and
financial hardship. The 2015 State Bank of Pakistan Quarterly Review Report
demonstrates the deteriorating asset quality of Pakistan's banking industry. In June of
2015, their non-performing loan balance rose by 1.6 percent. The market's fierce
competition and deregulation have resulted in substantial interest rate volatility, which
can have a dynamic impact on costs and earnings and increase the risk associated with
interest rates. (Steve, 2021).

1.2. Background
Major categories of risk encountered by financial institutions. Furthermore, lenders
are exposed to this specific risk due to borrowers' limited repayment capabilities,
placing savers' funds at risk and potentially causing significant losses for banks,
leading to financial instability that hampers economic growth and progress. Banks are
 2

not able to evade this particular risk because it is connected to their primary function
of providing credit. Therefore, banks are continuously working to lessen these risks
by creating credit policies that improve loan quality and decrease the number of non-
performing loans. The likelihood of financial crises increases as the credit risk at
banks grows, and decreases as it diminishes (Brown, 2021). Hence, banks aim to
minimize these risks by verifying that borrowers possess collateral and assets that
surpass the loan amount in ratios dictated by the bank, considering various factors
such as directives from central banks. Additionally, the bank must effectively handle
credit risk in order to ensure the bank's business performance, sustainability, and
longevity, as the primary source of income for banks comes from the interest margin
earned through lending operations.

1.3. Variables Description

Credit Risk
Different definitions of credit risk have been given in the literature. For example,
Fredrick (2019), Kaaya and Pastory (2019), and Nawaz et al. (2019) defined credit
risk as the risk of a borrower's failure to meet the terms of a line of credit with a bank.
Given the importance of the credit creation process for survival, credit risk
management is inevitable for long-term success and for the reduction of default
losses. Credit risk can have a positive or negative impact on performance. Banks need
to implement an integrated credit risk management strategy to mitigate the risk
inherited by individual credits as well as for their portfolios. Effective and sound
credit risk management calls for proper identification, measurement, and monitoring.
(Kaaya and Pastory, 2019); Musyoki and Kadubo, 2017; Nawaz et al., 2020).
Credit risk is the probable risk that a bank presumes when extending a loan, line of
credit, or other form of financing to a borrower. Banks deploy a robust credit risk
management system to identify and manage default risk which means that banks aim
to improve their ability to detect, understand and monitor credit risk to avoid default.
Hence, investing in robust credit risk management to increase the chances of avoiding
default is essential for banks to maintain a good and safe financial position (Rakhaev,
2020). It is often backed by many research on research and the effectiveness of credit
risk management is being conducted on the bank performance. (Bhatt et al., 2023).
 3

Effective credit risk management in banks and the skills to address credit risks have a
substantial impact on the profitability and overall success of these financial
institutions. (Athanasoglou et al., 2019). By collecting the surplus from savers and
lending the money to borrowers, the business acts as an intermediary. Banks may
receive a high interest rate in exchange. (Khan et al., 2020; 2015).
Managers that practice competent and effective risk management can optimize the
value of the b ankingindustry and improve the asset efficiency of their firm. (Gupta et
al., 2018).

Liquidity Risk
Liquidity risk is caused by conflicting in the maturities of assets and liabilities and
shortfalls in the capital funds mix. These factors do not have a positive effect on
banks' financial performance. The banks are unable to promptly liquidate a holding.
(Arif & Anees, 2018). Effective liquidity management is a key factor of the financial
performance of traditional banking organizations. Implementing an effective liquidity
management strategy empowers financial institutions to meet their funding needs and
commitments while ensuring sufficient liquidity reserves to withstand unexpected
liquidity disruptions. The importance of liquidity management on the performance of
conventional banks can be significant within this particular context (Cornwell et al.,
2023).
Realizing that all financial organizations frequently face common financial dangers is
an interesting realization. For example, there are certain risks that banks and
microfinance firms have in common. These risks are usually related to credit and
liquidity. Risk to liquidity, as declared by (Jenkinson, 2008). involves a scenario in
which a bank fails to fulfill its obligations Debts must be repaid quickly because
creditors may request their money back unexpectedly. This ultimately results in the
urgent sale of assets, which consequently negatively impacts the bank's profitability.
(Chaplin, Emblow & Michael. 2020).

Economic growth and Stability

Realizing that all financial organizations frequently face common financial dangers is
an interesting realization. For example, there are certain risks that banks and
 4

microfinance firms have in common. These risks are usually related to credit and
liquidity. Risk to liquidity, as declared by
(Jenkinson, 2008). involves a scenario in which a bank fails to fulfill its obligations
Debts must be repaid quickly because creditors may request their money back
unexpectedly. This ultimately results in the urgent sale of assets, which consequently
negatively impacts the bank's profitability. (Chaplin, Emblow & Michael. 2020).
An important part of a bank's agenda has always been risk management. In order to
offer their customers financial solutions, banking institutions take on a variety of
financial risks. As such, they are essential as intermediaries, offering expertise in any
given market, financial resources, and operational efficiency. Banks typically hold a
prominent place in these transactions because of their significant function
(Santomero, 2017). The banking sector has always played a major role in both
economic growth and human welfare, regardless of a few crises over the years. Banks
act as a facilitator, offering their customers solutions for money liquidity. In order to
accomplish this, banks occasionally utilize their equity to facilitate transactions and
absorb risks.(Santomero, 2017).

Financial Performance (FP)

Banks try to boost their financial performance (FP) by issuing loans while playing
their mediator role; banks have a high chance of facing credit risk. Accornero et al.
(2018) found that the country’s banking industry mostly faces failure due to high
credit risk. Sometimes, it leads to failures of the whole financial system. Credit risk is
predicted to rise when a borrower cannot meet their obligation about future cash
flows. Commercial banks’ FP is affected by two factors: one is external and the other
is internal. Bank-specific factors are internal and able to control factors of the
commercial banks.
Additionally, the financial performance of banks is negatively impacted by non-
compliance with regulations and ineffective risk management practices. Nevertheless,
to improve financial performance, the banking industry in Pakistan has adopted
modern methods (internet banking) to conduct financial activities, which increases the
risk of customer loss and default credit risk.
(Ongore & Kusa, 2018).
 5

1.4. Problem Statement

The financial performance of commercial banks is vital to the stability of the banking
sector and the economy. Pakistan's banking sector faces a dynamic operating
environment characterized by fluctuating economic performance, credit risk
management challenges and changing liquidity conditions. Although it is widely
accepted that credit risk and liquidity risk management play a key role in determining
a bank’s financial performance, how these factors along with macroeconomic
indicators such as inflation, GDP growth and interest rates affect the performance of
Pakistan’s commercial banks has yet to be thoroughly investigated (Iqbal & Mirza,
2020).

Recent research has highlighted the importance of credit risk in influencing banks’
profitability and stability, as non-performing loans (NPLs) greatly impact capital
adequacy and operational efficiency (Iqbal & Mirza, 2020). Moreover, managing
liquidity risk has become a vital area of emphasis, as inadequate liquidity
management can result in insolvency during periods of economic distress (Ahmed &
Niazi, 2022).

This research seeks to explore the combined effect of credit risk, liquidity risk
management, and macroeconomic factors on the financial performance of commercial
banks in Pakistan. The aim is to offer empirical insights into how these factors
interact and affect essential performance metrics like profitability, return on assets
(ROA), and return on equity (ROE). (Aslam, A., & Ali, I. 2023).

1.5. Research Objectives:

The purpose of this study is to identify, evaluate, and present actual data regarding the
issue facing Pakistan's commercial banks. Much researches have indicated that credit
risk and liquidity risk have a negative effect on the performance of commercial banks.

• To investigate the relationship between Credit risk and the financial

performance of commercial banks.

• To investigate the relationship between Liquidity Risk and the financial

performance of commercial banks.
 6

• To investigate the relationship between macro-economic factors (GDP,

Inflation, Interest rate) and the financial performance of commercial banks.
1.6. Research Questions
To achieve the above discussed research objectives, the following research questions
will be addressed:
Q1: What is the relationship between Credit Risk and the financial performance of
commercial banks?

Q2: What is the relationship between Liquidity Risk and the financial performance of
commercial banks?

Q3: What is the relationship between GDP and the financial performance of
commercial banks?

Q4: What is the relationship between Inflation and the financial performance of
commercial banks?

Q5: What is the relationship between Interest rate and the financial performance of
commercial banks?

1.6. Scope of the Study

The scope of this study is to determine how financial risk management affects
Pakistani commercial banks' ability to predict their financial performance. Upon
closer inspection, literature has a few holes and flaws. The research has strengthened
the risk management theory's homogeneity presumptions for Pakistani commercial
banks. Since there are not many studies in this field, the study adds to the body of
empirical information by examining various risk management strategies and the
financial performance of commercial banks in developing nations like Pakistan.
Initially, the impact of different financial risk management methods on the financial
performance of Pakistani banks is determined. These tools include credit risk, interest
rate risk, and liquidity Ongore & Kusa, 2018).
Second, a two-step system regression method is applied to a dynamic panel in order to
test the previously described association. The banking industry in Pakistan depends
heavily on financial risk management since it raises company value. Therefore, it is
 7

important to concentrate on risk management techniques, particularly in the banking

industry, as this enhances their financial performance. (Tursoy & Faisal, 2018).
Commercial banks' financial performance varies significantly depending on their
financial risk management profile. Bank loans and numerous other sources expose
banks to credit risk. The Basel 1 committee has put in place efficient procedures to
address this kind of risk, and the board of directors examines the credit risk
management plans. The Pakistani banking sector must consider operationalizing and
putting into practice appropriate financial risk management methods in order to
address the main issues it faces.
 8

CHAPTER NO 2
LITERATURE REVIEW
2.1. Theoretical Study
Agency Theory
Agency theory investigates how bank shareholders (principals) interact with managers
(agents). In the credit risk context, agency issues occur when bank managers, whose
pay is linked to short-term profits, engage in too much credit risk to boost earnings,
possibly harming the bank's long-term stability (Chike and Ebere,.2023; Duho et al.,
2023). This may lead to:

• Taking too much risk can result in greater credit losses and harm the overall
financial performance in the long run.
• If stakeholders view the bank as too risky or poorly managed, it could result in
damage to its reputation and a decrease in market value.

Multiple tools are available to address agency issues. The owner of the organization
might decide to offer loyal managers rewards in the form of shares, giving them
improved chances for advancement and guaranteed employment. Thus, the managers
can make decisions that increase the organization's performance and bring in profits.

Liquidity Return Trade-off Theory

The theory of Liquidity-Return Tradeoff suggests that banks have to manage the
balance between liquidity (meeting short-term obligations) and seeking higher returns
(from lending and investing in less liquid assets). Banks that have more liquidity
might not achieve higher profits, while banks with lower liquidity could be at risk of
experiencing financial difficulties when the market is chaotic (Shleifer, A., & Vishny,
R. W., 2020).

Implications for Financial Performance:

Banks that have superior performance during the times of financial crises, liquidity
management tend to be more robust, leading to sustained stability in the long run.
 9

Nevertheless, banks with high liquidity levels could experience reduced profitability
because of the relatively low yield on liquid assets such as government bonds.

Inflation & Profitability Theory

According to this theory, an important factor in banks' financial performance is
inflation. Macroeconomic factors like GDP growth, inflation, and interest rates
significantly influence a bank’s financial outcomes. On the one hand, modest inflation
can boost the demand for nominal loans and raise collateral values, which will
increase profitability. However, excessive inflation can lower actual loan demand,
raise funding costs, and devalue assets (Zhou, X., & Li, C. 2021).

Implications for Financial Performance:

Moderate inflation may help banks by allowing them to raise interest rates on
borrowers in order to cover increased costs. However, strong inflation can lower real
earnings and raise default risks, which hurts bank profitability, especially if it happens
unexpectedly.

2.2. Empirical Study

Commercial banks' financial performance varies significantly depending on their
financial risk management profile. Bank loans and numerous other sources expose
banks to credit risk. The Basel 1 committee has put in place efficient procedures to
address this kind of risk, and the board of directors examines the credit risk
management plans (Muhammed, 2020). These findings, however, are at odds with
earlier research that revealed a negative correlation between credit risk and Nigerian
banks' performance and a significant correlation between capital sufficiency and
performance (Frederic, 2019). Muhammed (2020) However, Kolapo et al. (2021)
discovered a direct and favorable correlation between these two factors. They
proposed that effective risk management might boost banks' productivity. (Drehmann,
Sorensen and Stringa, 2020) highlighted the significance of credit risk, one of the
biggest threats that banks face. The authors claimed that in addition to default risks,
the general quality of credit, off-balance sheet assets, liabilities, and the book's
 10

repricing characteristics all affect a bank's profitability and net worth. The bank's
capacity to anticipate, avoid, and assess risks determines how profitable it will be.
Considering this, it has been noted that more and more banks are overstretching their
current capacity for human resources (Saunsi, 2022).
According to a study, certain public sector banks in India's financial performance is
significantly impacted by credit risk management indicators. The empirical evidence
indicates that profitability, or ROA, has a negative correlation with liquidity and AQ
but a positive correlation with CAR, management quality, and earnings ability (Ali
and Dhiman, 2019).
The adoption of Islamic finance by banks in managing credit risk constitutes a new
link with Islamic finance which promotes entrepreneurship. In the lack of Islamic
finance, start-ups have a hard time raising capital. High debt costs cripple their
potential, forcing them to turn to conventional financing. Contrasted with, Islamic
finance is a participation, a fair alternative means of traditional finances, and shares
goals. Risk with entrepreneurs. However, lack of support for regulatory environments
Islamic financial institutions are encouraging to accept expensive approaches for
compliance. Compliance costs to Islamic Bank, thus transfer of high costs, especially
by entrepreneurs. For the background of the default high -velocity because of the non
-working loan. (Arshed et al., 2023).
Banks concentrate on their regions that are more vulnerable to risk and develop plans
to mitigate that risk. To improve their ability to recognize and reduce risk, risk
managers receive ongoing training. To assess and lower the degree of risk, banks have
developed specific risk management frameworks (De Juan, 2021).
Since banks deal with money, they are subject to liquidity risk because they need cash
and liquid assets to operate. To finance expenses and act as a buffer against future
uncertainties, money is required in the form of liquid assets and pure purchasing
power. High liquidity assets, such as cash, in the banking industry, however, translate
into low returns and higher opportunity costs for money holding. Therefore, banks are
not allowed to hold large amounts of liquid cash unless specifically required by the
regulating body. But keeping cash on hand also helps to maintain the stability of the
financial system (Chowdhury & Zaman, 2022). The financial performance of a bank
is determined by several things. There are some macro factors that apply to the entire
 11

sector. However, depending on a bank's level of establishment, their impact differs

from bank to bank. The macro factor encompasses national progress indicators such
as GDP, inflation, interest rates, and political situations. An increasing GDP has a
beneficial impact on profitability. Likewise, times of political stability and business
cycle expansion have a favorable impact on banks (Athanasoglou et al., 2023).
Bani Yousef et al. (2023) focus researchers' attention on another type of financial risk:
market risk, which arises from uncertain market conditions and affects all companies.
Market risk is an unavoidable risk and cannot be diversified. This risk arises due to
uncontrollable factors like war. Unforeseen inflation, changes in interest rates and
exchange rates, political events.Market conditions affect the bank's revenues, thus
affecting its profits.
The performance of banks is improved when interest rates rise. Five major Pakistani
commercial banks' financial performance and interest rates were determined to be
significantly correlated negatively and indirectly by other researchers (Waseem &
Abdul, 2024). It was agreed that the banks' performance would suffer if interest rate
risk was not controlled or avoided. Nonetheless, Islamic banks' performance is
positively impacted by their risk profile. (Zainol & Kassim, 2020).
The bank's capacity to anticipate, avoid, and assess risks determines how profitable it
will be. According to reports, a growing number of banks are overstretching their
current human resource capabilities considering this. This has resulted in a few issues,
such as financial crimes, a subpar credit appraisal system, and the accumulation of
bad credit reflected by (Sanusi, 2022). As a result, an increasing number of banks are
experiencing trouble and are collapsing due to the pressure to perform. The author
also noted additional elements that contribute to these systemic failures of banking
intuitions, including bad management, opposing ownership effects, and other forms of
insider exploitation, in addition to political worries and drawn-out legal proceedings
pertaining to debt collection.
Abiola and Olausi, (2019). The influence of credit risk management on banks'
profitability was clarified by another study. According to the study's findings, there is
a strong correlation between credit risk management and banks' efficacy. Similarly,
Cooper et al. (2003) hypothesized that changes in credit risk could mimic changes in
 12

the health of the banks' credit portfolio, which could potentially affect the banks'
performance.
(Norazwa et al, 2018) have employed a variety of liquidity risk metrics and
investigated how liquidity risk affects the performance of banks in Bahrain and
Malaysia. They discovered a substantial inverse relationship between deposit
volatility and liquidity risk. Nonetheless, they discovered a strong and favorable
correlation between liquidity risk and bank capitalization.
According to the liquidity management theory, a bank may not always need to have
highly liquid assets on its balance sheet because it can always buy money from the
market when it needs it. Many scholars disagree with this notion because they contend
that banks may not be able to find the necessary liquidity at a time of low business
and low profitability because creditworthiness may be low and market trust may have
been eroded. Nonetheless, deposits and other creditors on the liabilities side of the
balance sheet could provide liquidity for well-established banks (Nwankwo, 2021).
While the liquidity coverage ratio has no discernible effect on financial performance,
liquidity risk as shown by the net stable funding ratio tends to impair it. Liquidity may
suffer as the net stable funding ratio rises, but performance may also suffer (Murithi
& Waweru, 2017).
The essence of this theory is that investors are risk averse. The theory proposes the
concept of strategic loan portfolio management that helps to avoid risks and improve
the performance of banks. To this end, banks need to carefully mix different types of
loans to maximize profitability. Diversification of loan portfolio practice is the best
tool to maximize profitability. Loans evaluate weaknesses and power Financial Bank
Sector from a future perspective. On the other hand, it is necessary to sell with a title
before accepting the capital (Khan et al., 2023).

2.3.Research Hypothesis
The research Hypothesis statements of our independent and dependent variables are
discussed below:
• H1: Credit Risk has a significant relationship with the financial performance
of commercial banks.
 13

• H2: Liquidity Risk has a significant relationship with the financial

performance of commercial banks.
• H3: GDP has a significant relationship with the financial performance of
commercial banks.

• H4: Inflation has a significant relationship with the financial performance of

commercial banks.

• H5: Interest rate has a significant relationship with the financial performance
of commercial banks.

2.4.Conceptual Framework
Figure 1
 14

Liquidity Risk

Credit Risk

Financial
GDP
Performance

Inflation

Interest Rate

CHAPTER NO 3
RESEARCH METHODOLOGY

3.1. Research Approach

In this research, we examine the impact of Credit Risk and liquidity Risk
Management on the performance of Commercial banks in a developing nation i.e.
Pakistan. We used the quantitative data and Secondary data approach in this research
to analyze the different macroeconomic factors used in this research paper. We used
different independent variables i.e. Credit Risk, Liquidity Risk and macro-economic
 15

factors such as GDP, Inflation and interest rate. Dependent variable is the financial
performance of Commercial banks.

3.2. Research Design

In this research we have used the Secondary method of data collection. By the help of
this above discussed method, we will examine different articles, opinions,
experiences, analysis, reports and different research regarding the influence of Credit
Risk and Liquidity Risk on the financial performance of the commercial banks in
Pakistan.

3.3. Research Population

According to the population of our research study, the research population will include all
the commercial banks in Pakistan. The population includes all the assets and other items as
we mainly relied on the total assets of banks for the ratio’s calculations. This encompasses
both small or big famous banks. We also perform Descriptive test and Correlation test on the
data for the results.

3.4. Measurement of Variables

Table 1

Variables Proxies Measurement Evidence

Financial • Return on Equity •Net profit / Total Mubin et al.

Performance equity (2014)
(ROE)
 16

(FP) • Return on Assets •Net profit / Total Khadafi et al.

assets (2014)
(ROA)

• Credit Risk (CR) • Capital Adequacy • Higher the ratio, MingdongYe

higher the risk (2012)

• Liquidity Risk •. Liquidity Coverage • High quality Liquid Duijm, P., &
(LR) Wierts,
Ratio Assets / Net
Cashflows P. (2016)

3.5.Sample Size
We have chosen six banks for the study's sample we choose selective banks across all
the commercial banks in Pakistan based on the rankings according to the KPMG
research data. Then from the choosen banks we took total assets as the base value for
our calculations. Total assets are a good way to measure a bank's size, financial
stability, and market presence, this standard was chosen to assess the banks'
performance and strategic choices. Because they are some of the biggest banks in the
business, the chosen banks guarantee a thorough representation of organizations that
have a big impact on the financial sector. A strong temporal framework for examining
trends, patterns, and the consistency of important financial indicators is provided by
the sample's inclusion of data from the previous five years. The study's focus on banks
with sizable total assets is intended to guarantee that the results are applicable to
comprehending large-scale banking operations, which usually establish industry
standards. This strategy preserves the study's applicability to both academia and
business while allowing for a targeted but thorough analysis (KPMG, 2024).

The banks selected are:

• Habib Bank Limited

• Faysal Bank
 17

• Allied Bank

• Bank Alfalah

• United Bank Limited

• National Bank of Pakistan

• HBL Bank (total assets: 30,119,216 million)

• Faysal Bank (total assets: 1,371,285 million)

• Al Baraka Bank (Total assets: 798,791 million)

• National Bank of Pakistan (NBP) (total assets: 6,668,874 million)

• Allied Bank (total assets: 654,791 million)

• United bank Limited (UBL) (total assets: 881,890 million)

(KPMG, 2024)

3.6.Data Analysis
The panel data used for this study's data analysis blends cross-sectional and time-
series dimensions, enabling a thorough assessment of the chosen banks over a five-
year period. To evaluate performance and spot trends, we conduct Descriptive
Statistics and Correlation test analysis for the computation of results and important
financial ratios. Furthermore, we have also analyze through the graphical and visual
representation of the relation of dependent variables with Independent variables. We
have also analyzed the relationship by the degree of correlation in the graphical or
visual representation offering more profound understanding of the elements impacting
bank financial performance.
 18

CHAPTER NO 4
DATA ANALYSIS AND DISCUSSIONS

4.1.Descriptive Analysis
Table 2

N Minimum Maximum Mean Std. Deviation

ROA 30 .0072 .1354 .037390 .0339975

ROE 30 .0051 .2988 .142437 .0831934
CREDIT RISK 30 .1369 .2671 .187943 .0398155
LCR 30 1.4060 3.3510 2.063743 .5529710
GDP 30 -.0400 .0600 .021340 .0369291
 19

INFLATION 30 .0800 .2970 .171680 .0839896

INTEREST
30 .0700 .2200 .136000 .0528156
RATE
Valid N (listwise) 30

Interpretation

In this table 4.1, ROA and ROE are the dependent variables and Credit risk, liquidity
risk, GDP, Inflation and Interest rates are the independent variables. The descriptive
analysis of the variables in our model shown that the total number of observations that
we took for the data collection is 30. The average value of variable ROA is 0.37390,
within a range of 0.0072 to 0.1354. The SD which is 0.339975 for the 6 commercial
banks we took from the period of 2019-2023, is used to measure the variability in the
data on ROA. ROE has the average value of 0.142437, within a range of 0.0051 to
0.2988. For the 6 Commercial banks we took for the years 2019-2023, has the SD
value for variability is 0.831934. Credit Risk has the central value of 0.187943 with
the minimum value of 0.1369 and the maximum value of 0.2671. For all the 6
commercial banks we took for the years 2019-2023, has the SD value which is used to
represent the variation from the mean value is 0.0398155. The mean value of LCR is
2.063743 with a range of 1.4060 to 3.3510. For the 6 Commercial banks we took for
the years of 2019-2023, its variability around the central value is 0.5529710. The
mean value of GDP is 0.021340 with a minimum value of -0.0400 to the maximum
value of 0.0600. For the 6 Commercial banks we took for the time period of 2019-
2023, the SD equals to 0.0369291 which represents variability in the data. The mean
value of Inflation is equals to 0.171680 with a range of 0.0800 to 0.2970. For the 6
Commercial banks we took for the time period of 2019-2023, has the SD equals to
0.0839896 which represents the variability among the data in the model. The mean
value of Interest rate is equals to 0.136000 with a minimum value of 0.0700 to the
maximum value of 0.2200 in the table above shown. The Standard deviation for this
data variability is equals to 0.0528156 for the 6 Commercial banks we took for the
period of 2019-2023.
 20

4.2.Correlation Statistics
Table 3

Correlations

CREDIT INFLAT INTERES

Control Variables ROA ROE RISK LCR GDP ION T RATE

BANK ROA Correlat -.643*

1.000 *
.193 -.196 .012 .159 .094
GROWT ion
H ROE Correlat -.643*
*
1.000 -.071 .214 -.139 .208 .213
ion

CREDIT Correlat
.193 -.071 1.000 -.189 -.332 -.001 -.009
RISK ion

LCR Correlat
-.196 .214 -.189 1.000 -.064 .065 -.042
ion

GDP Correlat
.012 -.139 -.332 -.064 1.000 .227 .240
ion

INFLATION Correlat
.159 .208 -.001 .065 .227 1.000 .956**
ion

INTEREST Correlat
.094 .213 -.009 -.042 .240 .956** 1.000
RATE ion

**. Correlation is significant at 0.01 level

Interpretation

Table 4.2 shows the Correlation analysis of all the study’s variables. Correlation is a
statistical method of determining how closely or strongly two variables are related to
each other. In this table, variable ROA has a significant i.e. positive relationship with
the variable ROE at 1% level of significance. Similarly, ROA has also a significant
relationship with variable Inflation at 1% level of significance in the table. Similarly,
ROA has also a significant relationship with variable Interest rate at 1% level of
significance in the table. Now if we talk about the 2nd variable ROE, it has a
significant i.e. positive relationship with the variable ROA at 1% level of significance.
Similarly, the variable Inflation has also a significant i.e. positive relationship with the
variable Interest rate at 1% level of significance. Similarly, the variable Interest rate
has also a significant i.e. positive relationship with the variable Inflation at 1% level
of significance.
 21

4.3.Graphical or Visual Representation of Variables Data

Figure 2

ROA Relation With Credit Risk

30.00%

25.00%

20.00%
Credit Risk

15.00%

10.00%

5.00%

0.00%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00%

Return on Assets (ROA)

Interpretation

ROA relation with Credit risk:

Observation: From left to right, points appear to have no pattern.

This suggests that credit risk and return on assets (ROA) have a positive relationship
between eachother but insignificant relation. ROA has a positive relation means often
does credit risk. The points are clearly in no pattern or direction but the points are
closely follow the line means points are not so close to the slope line also suggesting
that there is moderate relationship between variables.
A study featured in the "Journal of Advertising" revealed that despite businesses
pouring substantial amounts into advertising, there was a poor relationship between
advertising expenditures and actual sales growth. This implies that elements like
product quality or brand reputation could play a more critical role in influencing
consumer buying choices (Brown, 2019).
Another study featured, suggested a limited connection between educational
attainment and income levels among specific demographics. Although education is
typically linked with increased earnings, the research revealed that for certain groups,
 22

elements such as industry, geographical location, and personal networks had a more
significant impact (Harminson, 2021).

Figure 3

ROA Relation With Liquidity Risk

400.00%
350.00%
300.00%
Liquidity Risk

250.00%
200.00%
150.00%
100.00%
50.00%
0.00%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00%

Return on Assets (ROA)

Interpretation

ROA relation with Liquidity risk

Observation: The points show a upward slope but not significant.

In this ROA and liquidity risk have a negative relationship between eachother and it is
not significant relation. T points are not significantly following the slope line or there
is no pattern which means that their is no correlation between the variables. The
points are not close to the line of slope but they are little far from slope line as
compare to previous graph, so the strength between the variables are so weak.
A piece in the "Financial Analysts Journal" explored the link between
macroeconomic indicators, such as GDP growth and inflation, and stock market
performance, finding that the correlation was frequently weak. This implies that stock
prices could be affected by alternative factors, including investor sentiment and
market speculation (Steve, 2019).

Another research article examined how physical activity relates to different health
outcomes, revealing a weak connection in specific groups. This indicated that
 23

elements like nutrition, genetics, and economic status could significantly influence
health more than physical activity by itself (Steve, 2022).

Figure 4

ROA Relation With GDP

8.00%
6.00%
4.00%
2.00%
GDP

0.00%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00%
-2.00%
-4.00%
-6.00%

Return on Assets (ROA)

Interpretation

ROA relation with GDP

Observation: Points have a slight dispersion but slope upward not significantly
although.

In this ROA and GDP have a positive relation between eachother but not significant
relation. Although the association is weaker in this graph, but there seems to be a no
pattern in the graph along slope line. ROA may be influenced by GDP, although
variability is probably caused by other reasons as well. The points are not clustered
and do not follow a line closely, the strength is very weak between the variables in
this.

In previous articles, some theories or researches in the favor of results shown are
discussed so, previously discussed researches are also applicable or valid in this
situation also.

Figure 5
 24

ROA Relation With Inflation

35.00%

30.00%

25.00%
Inflation

20.00%

15.00%

10.00%

5.00%

0.00%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00%

Return on Assets (ROA)

Interpretation

ROA relation with Inflation

Observation: The points are scattered, with no obvious pattern.

In this, ROA and Inflation have a positive relationship between eachother and a
significant relation between them. Changes in inflation have a significant influence on
ROA. The points are not clustered and do not follow a slope line closely hence, the
trength is weak among the variables.
Weak correlation of 0.159 is shown above in the correlation results. And also in this
situation of no relationship among variables, previously discussed theories or
researches are also applicable or valid in this situation also.

Figure 6
 25

ROA Relation With Interest Rates

25.00%

20.00%
Interest Rates

15.00%

10.00%

5.00%

0.00%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00%

Return on Assets (ROA)

Interpretation

ROA relation with Interest rates

Observation: The points although little bit slope upward but not make a visible
pattern.
Interest rates and ROA have a positive relationship between eachother and have a
significant relation between them. A rise in the ROA is correlated with higher interest
rates. The points are clearly not in pattern but to some extent they are close to follow
the line, hence the strength of correlation is moderate among the variables.
Similarly, in this previously discussed theories or researches of same situation are also
applicable in this scenario as well.

Figure 7
 26

ROE Relation With Credit Risk

30.00%

25.00%

20.00%
Credit Risk

15.00%

10.00%

5.00%

0.00%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%

Return on Equity (ROE)

Interpretation

ROE relation with Credit risk

Observation: Points have a small upward slope, as observed.

ROE and Credit Risk have a negative relationship between eachother but not a
significant relation between them. But this indicates a favorable correlation means
that there is a relationship among the variables. ROE slightly rises as credit risk does.
The points are clearly clustered and closely follow the slope line, the strength of
correlation among the variables is strong.
Various studies have demonstrated a positive link between education and income. For
example, research featured in the "Economics of Education Review" discovered that
individuals with greater educational qualifications typically earn considerably more
throughout their lives than those with lower educational backgrounds. This connection
emphasizes the significance of education in enhancing economic prospects
(Muhammad Anees, 2022).

Figure 8
 27

ROE Relation With Liquidity Risk

400.00%
350.00%
300.00%
Liquidity Risk

250.00%
200.00%
150.00%
100.00%
50.00%
0.00%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%

Return on Equity (ROE)

Interpretation

ROE Relation with Liquidity risk

Observation: The points have a sharp upward slope.

There is a strong positive relationship between ROE and liquidity risk, but not a
significant one. Similarly to the relationship of last graph of ROE & Credit risk, the
points are close to the slope line which also suggesting that the strength of correlation
among the variables is very strong.
A study in the "Journal of Clinical Psychiatry" support in this situation that a strong
relationship between regular physical activity and improved mental health outcomes.
The research indicated that individuals who engage in regular exercise report lower
levels of anxiety and depression, suggesting that physical activity plays a crucial role
in enhancing mental well-being.

Figure 9
 28

ROE Relation With GDP

8.00%
6.00%
4.00%
2.00%
GDP

0.00%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%
-2.00%
-4.00%
-6.00%

Return on Equity (ROE)

Interpretation

ROE Relation with GDP

Observation: The points are scattered and no pattern was shown in this graph.
ROE and GDP have a negative relationship between eachother, but not a significant
relation. The points are not clustered and also do not follow or not close to a slope
line, which suggests the strength of correlation among the variables are also weak.
Another research published in “Cyberpsychology, Behavior, and Social Networking”
indicated a relationship between social media use and self-esteem. The study found
that individuals who actively engage with supportive online communities experience
higher self-esteem compared to those who do not participate, highlighting the impact
of social interactions on self-perception (Et.val, 2020).

Figure 10
 29

ROE Relation With Inflation

35.00%
30.00%
25.00%
Inflation

20.00%
15.00%
10.00%
5.00%
0.00%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%

Return on Equity (ROE)

Interpretation

ROE Relation with Inflation

Observation: The points scatter without a clear pattern.

ROE and Inflation have a positive relationship between eachother but not a significant
relation. The points are not clustered but points are close to the slope line which
suggests that the strength of correlation among the variables is strong. Similarly, in
this situation above or previously discussed theories or researches are applicable in
this also.

Figure 11
 30

ROE Relation With Interest Rates

25.00%

20.00%
Interest Rates

15.00%

10.00%

5.00%

0.00%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%

Return on Equity (ROE)

Interpretation

ROE Relation with Interest rates

Observation: A small upward trend is formed by the points.

ROA and Interest rate have a positive relationship between them but not a significant
relation. The points are clearly clustered and closely follow or close to the slope line,
which suggests that strength of correlation is strong between variables. However,
previously discussed theories and researches in this same situation is also applicable
and valid in this.
 31

CHAPTER NO 5
CONCLUSION AND RECOMMENDATIONS

5.1. Conclusion
In this study, the causal relationship between credit risk, bank-specific characteristics,
and the financial performance of commercial banks in Pakistan is empirically
investigated. According to the study's findings, managers in Pakistan should
concentrate on boosting capital adequacy to improve monetary gain (FP) and reduce
credit risk by putting contemporary and efficient credit risk management approaches
and tactics into practice. Financial performance of commercial banks is relate with
credit risk and liquidity risk. On the other hand, the FP of South Asian commercial
banks is significantly and favorably correlated with CAR and LCR. All study control
factor or variable is significant in the model, which include bank size. There are
various policy implications for Pakistan’s commercial banks. The market's issues, a
lack of consumer awareness regarding loans, and a lack of customer oversight and
monitoring are the main causes of the rising Credit risk and Liquidity risk. When
determining whether their clients have a feasible way to repay their debts, bank
management should act quickly. Additionally, banks can provide professional loan
advisors with expert advice on practical ways to effectively fund the borrowing to
ensure the necessary return on the entire amount of money invested by the company.
Maintaining a effective and efficient liquidity position is essential for the commercial
to thrive, even in highly competitive environments.

The study's focus is solely on commercial banks, however Islamic banks can also use
this concept. This model can also be used by future researchers to compare
commercial and Islamic banks. This study's data was gathered from just six banks;
future research could include more institutions and longer study periods. Additionally,
the results are a more accurate and dependable depiction of the population if there are
more banks, and the year number is higher.
 32

5.2. Limitations and Recommendations

Some important Limitations and recommendations of our study are discussed below:

● Due to the time constraint, we took the small sample size due to which our
results are not very strong and significant. In future we should take large
sample size in order to generate strong and significant model and results.
• Banks should have to construct and implement the effective and
comprehensive risk management framework in order to address or mitigate the
Credit risk and Liquidity risk.
• In order to address or manage the credit risk effectively and comprehensively,
banks should have to diversify their loan portfolios across different sectors in
organizations.
• Banks should also have to develop effective and comprehensive liquidity risk
management policies in order to manage liquidity risk and to harm the effect
on financial performance of banks.
• Banks should have to closely monitor the macroeconomic factors such as
GDP, Inflation, interest rates as these factors plays a major role in the financial
performance of the bank.
• Banks should also have to develop some advanced technological policies
which will benefit in the financial performance of the commercial banks.

By implementing these above-mentioned recommendations commercial banks can

more effectively manage the credit risk, liquidity risk and other macroeconomic
indicators. These steps will also majorly help commercial banks in improving their
financial performance
 33

REFERENCES
● Bessis, J. (2020). Risk Management in Banking (Third edition ed.). John
Wiley & Sons. Retrieved from https://1lib.ae/book/2455823/4ff65a?
regionChanged=&redirect=221898158

● Athanasoglou, P. P., Brissimis , S. N., & Delis , M. (2022). Bank-specific,

industry-specific and macroeconomic determinants of bank profitability.
Journal of international financial Markets, Institutions and Money, 18(2), 121-
136. https://doi.org/10.1016/j.intfin.2006.07.001

● Kaaya, I., & Pastory, D. (2023). Credit risk and com- mercial banks
performance in Tanzania: Apanel data analysis. Research Journal of Finance
and Accounting, 4(16), 55-62

● Khan, M.A., Siddique, A. and Sarwar, Z. (2020), “Determinants of non-

performing loans in the banking sector in developing state”, Asian Journal of
Accounting Research.

● Ghosh, A. (2019), “Banking-industry specific and regional economic

determinants of non-performing loans: evidence from US states”, Journal of
Financial Stability, Vol. 20, pp. 93-104.

● Gupta, S., Pattillo, C. A., & Wagh, S. (2019). Effect of remittances on poverty
and financial development in Sub-Saharan Africa. World Development, 37(1),
104–115. https://doi.org/10.1016/ j.worlddev.2008.05.007

● Arif, A., & Anees, A. (2022). Liquidity risk and performance of banking
industry, Journal of monetary Regulation and Compliance, 20(2), 182–195.
https://doi.org/10.1108/135819 81211218342
 34

● Jenkinson, N. (2020). Strengthening regimes for controlling liquidity risk:

Some lessons from the recent turmoil. Bank of England Quarterly Bulletin,
Quarterly, 2, 223-228

● Bhatt, T. K., Ahmed, N., Iqbal, M. B., & Ullah, M. (2023). Examining the
Determinants of Credit Risk Management and Their Relationship with the
Performance of Commercial Banks. Journal of risk and financial management,
16(4), 235.

● Cornwell, N., Bilson, C., Gepp, A., Stern, S., & Vanstone, B. J. (2023).
Modernising operational risk management in financial institutions via data-
driven causal factors analysis: A pre-registered report. Pacific-Basin Finance
Journal, 77, 101906.

● Chaplin, G., A. Emblow, I. Michael, (2020). Banking System Liquidity:

Developments and Issues. Bank of England Financial Stability Review, (Dec),
93-112

● Accornero, M., Cascarino, G., Felici, R., Parlapiano, F. and Sorrentino, A.M.
(2018), “Credit risk in banks’ exposures to non-financial firms”, European
Financial Management, Vol. 24 No. 5, pp. 775-791.

● Ongore, V., & Kusa, G. (2023). Determinants of economic performance of

business banks in African country. International Journal of Economic Science
and Money Problems, 3(1), 237–252.

● Santomero, A. M. (2024). Commercial bank risk management: an analysis of

the process. Journal of Financial Services Research, 12(2-3), 83-115.

● Tursoy, T., & Faisal, F. (2019). The impact of gold and crude oil prices on
stock market in Turkey: Empirical evidences from ARDL bounds test and
 35

combined cointegration. Resources Policy, 55, 49–54.

https://doi.org/10.1016/j.resourpol.2017.10.014

● Muhammed, E. (2022). Credit risk and therefore the performance of Nigerian

banks knowledge base. Journal of Up To Date Analysis in Business, 2(1), 32–
39.

● Frederic, N. K. (2024). Factors affecting performance of commercial banks in

Uganda: A case for domestic commercial banks. Proceedings of 25th
International Business Analysis Conference African Nation, 1–19.

● Dang, U. (2021). The CAMEL rating system in banking supervision. A case

study.

● Ongore, V. O., & Kusa, G. B. (2023). Determinants of financial performance

of commercial banks in Kenya. International journal of economics and
financial issues, 3(1), 237.

● De Juan, A. (2021). From good bankers to bad bankers: Ineffective

supervision and management deterioration as major elements in banking
crises. World Bank.

● Chowdhury, M., & Zaman, S. (2019). Effect of Liquidity Risk on Performance

of Islamic banks in Bangladesh. IOSR Journal of Economics and Finance.

● Athanasoglou, P., Delis, M., & Staikouras, C. (2019). Determinants of bank

profitability in the South Eastern European region.

● Hasanov, F., Bayramli, N. and Al-Musehel, N. (2018), “Bank-specific and

macroeconomic determinants of bank profitability: evidence from an oil-
dependent economy”, International Journal of Financial Studies, Vol. 6 No. 3,
p. 78.
 36

● Ali, L., & Dhiman, S. (2019). The impact of credit risk management on
profitability of public sector commercial banks in India. Journal of Commerce
and Accounting Research, 8(2), 86.

● Sanusi, J. (2022). Central bank and the macroeconomic environment in

Nigeria. Quarterly Review, 3(3), 8-18

● Arshed, N., Sohail, H., & Gulzar, M. (2023). Investigating the Institutional
Quality Integration with Islamic Banking Development in Promoting
Entrepreneurship. Journal of Entrepreneurship and Business Venturing, 3(1).
https://doi.org/10.56536/jebv.v3i1.25

● Khawaja, I., & Musleh, D. (2019). Determinants of Interest unfold in Pakistan.

The Pakistan Development Review, 46(2), 129–143.
https://doi.org/10.30541/v46i2pp.129-143

● Zainol, Z., & Kassim, S. H. (2020). An analysis of Islamic banks’ exposure to

rate of return risk. Journal of Economic Cooperation and Development, 31(1),
59–84. https://www.researchgate.net/ publication/265261645

● Sanusi, J. (2022). Central bank and the macroeconomic environment in

Nigeria. Quarterly Review, 3(3), 8-18.

● Boudriga, A., Taktak, N. B., & Jellouli, S. (2019). Banking supervision and
nonperforming loans: A cross-country analysis. Journal of Financial Stability,
5(4), 361-380.

● Bani Yousef, A. N., Taha, R., Muhmad, S. N., & Zainul Abidin, A. F. (2023).
The impact of market risk exposure on banks’ financial performance: evidence
from the MENA region. Management & Accounting Review (MAR), 22(2),
229-251.
 37

● Abiola, I., & Olausi, A. S. (2019). The impact of credit risk management on
the commercial banks performance in Nigeria. International Journal of
Management and Sustainability, 3(5), 295-306.

● Norazwa, A., Mohamad, A., & Hawati, J. (2022). Liquidity risk and
performance: The case of Bahrain and Malaysian banks. World Economy and
Finance Journal, 8(2), 95–111. https://doi. org/10.21102/gefj.2015.09.82.07

● Murithi, J., & Waweru, M. (2017). Liquidity and monetary performance of

banks in African nation. International Journal of Social Science and Finance,
9(3), 256–266. https://doi.org/10.5539/ijef.v9n3p256

● Khan, N., Ramzan, M., Kousar, T., & Shafiq, M. A. (2023). Impact of bank-
specific factors on credit risk: Evidence from Islamic and conventional banks
of Pakistan. Pakistan Journal of Humanities and Social

● Sciences, 11(1), 580–592. https://doi.org/10.52131/pjhss.2023.1101. 0375

● SBP 2024 website, Articles, Current Landscape of the Banking Sector and
Emerging Trends.https://www.sbp.org.pk/70/sup-11.asp

● SBP, FSR 2023 Chapter 3https://www.sbp.org.pk/FSR/2023/Chapter-3.pdf

● SBP 2024 website –status of bank

branchhttps://www.sbp.org.pk/loaddata.html?val=all
38