Determinants of Banks Profitability in the Middle East and North Africa Region

Abstract

Bank profitability tends to go hand-in-hand with economic activity. Slower growth prospects may dent bank profitability through a reduction in the lending activity and a possible increase in credit impairments. This study identified the determinants of bank profitability for the Middle East and North Africa (MENA) region. Secondary data gathered from 10 countries, along with 927 observations spanning between 2008 and 2016, were analyzed in this study. The random-effect model was employed to assess the impact of several significant factors on bank profitability. As a result, non-interest income (NII) displayed a significantly positive impact on profitability. Essentially, increment in NII of banks (e.g., commission and credit card fee) positively affected the financial performance of banks and significantly contributed to the profitability of banks. Since economic growth had a positive impact on bank profitability, higher gross domestic product (GDP) led to higher profitability for banks across the MENA region. Nonetheless, a negative link was established between bank profitability and credit risk (non-performing loan or NLP). This signified that increment in NLP or low-quality loans adversely affected the financial performance of the banking segment. Hence, the banking sector in MENA should devise effective measures to increase NII earnings. More importantly, banks should be more risk-averse when providing loans to their clients.

1. Introduction

The significant role of banks in economic progress is undeniable stemming from their intermediary role between borrowers and lenders. Financial intermediaries work in the savings/investment cycle of an economy by serving as conduits to finance between the borrowers and the lenders. A bank is a financial intermediary that is licensed to accept deposits from the public and create credit products for borrowers. Banks generally make money by borrowing money from depositors and compensating them with a certain interest rate. The banks will lend the money out to borrowers, charging the borrowers a higher interest rate, and profiting off the interest rate spread (Yuksel et al., 2015). Besides, the banking segment offers employment opportunities to many by establishing branches throughout the country.

In the era of globalization, banks need to face multiple risks, including currency, credit, interest rate, and liquidity risks. The world has witnessed numerous poorly managed banks since the past two decades, which had led to the loss of jobs, bankruptcy of companies, and significant economic loss. It is essential for banks, thus, to effectively manage their assets and risks to attain a stable economy and reap profits (Dincer et al., 2016). As such, it is integral for studies to probe into bank profitability, to iron out economic risk factors.

Bank profitability denotes the difference between liability expenses and profitable assets. The literature depicts bank profitability as the function of macro and micro determinants. Macro variables, such as tax rate, inflation, gross domestic product (GDP), and interest rate, significantly affect bank profitability despite being unrelated to the internal processes. Meanwhile, micro variables, including non-performing loans (NLPs), bank size, expense and risk management, marketable securities, and capital, refer to bank-specific variables (Güngör, 2007). Many macro and microeconomic factors re-shape the balance sheet of the banking structure, thus amending their profitability equation.

Based on the financial intermediation theory, banking institutions assume an intermediary role as they match savers and borrowers in the economy. Banks mobilize funds from surplus spending units (SSUs) and provide these funds to deficit spending units (DSU) at a cost. Bank profitability is the difference in the interests charged by SSUs and interests charged to DSUs. Bank profitability is a key ingredient to forming banks’ resilience to distress and failure. Yuksel et al. (2018) asserted the important intermediary role of the bank between lenders and borrowers. Essentially, savers with excess funds may gain interest income (II), investors can borrow money to enhance business performance, and consumers may spend their future income – ultimately, banks are a catalyst to economic progress.

Prior studies had focused on the individual Middle East and North Africa (MENA) countries, such as Egypt (Omran, 2007), the banking performance in the MENA region (Olson & Zoubi, 2011), and the effect of ownership types (Farazi et al., 2011). However, only a few studies had identified the influential factors of bank profitability in the MENA region. Serving as the bridge that connects Asia and Europe, the MENA region is composed of 28 countries with a population of 578 million. It is rapidly developing while gaining significance in the global-scale economy due to its rich oil resources. The MENA region is home to the largest Islamic banks with the essential role of serving the Muslim and other communities worldwide. Some countries there, such as Jordan, Egypt, Morocco, and Tunisia, have established economic reforms for the past 35 years, thus liberating their financial systems (Naceur & Omran, 2011). Their banking sector, however, varies from one country to another.

Deregulation and financial liberalization of the oil-rich nation in the Gulf Cooperation Council (GCC) have transformed the oil-based economy into a market-based economy. In fact, hydrocarbon exports of the MENA countries accounted for 50% of the GDP. The GDP hiked to 6.5% from 4.0% for 2003–2007 due to the escalating oil price. Nevertheless, the 2008 global economic crisis had adversely affected the GDP of the GCC oil-rich nation.

Despite its economic significance, the Egyptian financial segment appeared to be less developed when compared to other GCC members (Omran, 2007). This was attributed to mostly family-owned banks and state-controlled specialized banks across Egypt (Naceur et al., 2009). Since the enactment of Egyptian Banking Law in 1975 (Central Bank of Egypt), acquisition and merger activities occurred in 2005 due to decreasing state role and escalating foreign participation.

As for Jordan, the government sought IMF aid after its currency depreciated due to the 1988–89 crisis. As a result, a stabilization program was initiated, which led to privatization, financial restructuring (Bdour & Al-khoury, 2008), and the removal of the maximum limit for foreign direct investment in 1997. The 2000 Banking Law required the Jordanian banks to comply with several terms (license and registration fee), apart from providing investment portfolio services and financial consultation.

This study significantly contributes to the body of knowledge. Initially, it probed into the influential factors of bank profitability. Next, since only a handful of studies had evaluated the MENA region concerning bank profitability, this study bridges the gap. Essentially, this study pioneers in assessing bank profitability in the MENA region based on the ordinary least square (OLS) and fixed effect panel regression techniques. Based on robust standard errors and cuttingedge techniques, several prescriptions are listed for banks in the MENA region to attain better profitability. The study outcomes may also serve as a guideline to policymakers in devising effective policies.

The remaining paper is as follows. Section 2 presents the literature review, while Section 3 depicts both data and methodology. Section 4 presents the outcomes and Section 5 concludes the study.

2. Literature Review

The literature review presented in this section focuses on the determinants of bank profitability. The aspect of bank profitability, although a popular subject matter among economic researchers, lacks investigation across the MENA region, particularly in identifying the related determinants.

Past studies had amplified the significance of macroeconomic factors in light of bank profitability. Al-Jafari and Alchami (2014), who assessed Syrian bank profitability using the Generalised Method of Moments (GMM) approach, revealed the effect of inflation rate (INF) on bank profitability. Upon examining the determinants of Islamic banking performance in the Middle Eastern region for 1993–1998, Bashir (2001) found that higher profitability was affected by higher loan to asset ratio and higher leverage. Besides, foreign banks gained more profits than local banks. Evidently, explicit and implicit taxes negatively influenced banking performance, while favorable macroeconomic conditions gave positive outcomes.

Prior studies had assessed both external and internal determinants of bank profitability. In developed economies, return on equity (ROE) and assets rely on net interest revenue/ margin, size, capital strength, and liquidity (Van Horen, 2007; Micco et al., 2007). Size emerged as a significant determinant of banking performance in numerous studies (Athanasoglou et al., 2008); whereby large-sized banks can gain better market access and better generate economic scales than smaller banks (Heggestad, 1997). Additionally, Alhassan et al. (2016), Menicucci and Paolucci (2016), and Regehr and Sengupta (2016) discovered a positive link between bank profitability and size based on varying techniques. On the contrary, Anarfi et al. (2016) and Kolapo et al. (2016) found the insignificant effect of bank size on the profitability of the banks.

Risk management is integral for the banking sector, as low liquidity and poor asset quality can cause banks to fail (Nandy & Lodh, 2012). An unstable economic period forces the banking sector to raise their liquid holding and diversify their portfolios, mainly to minimize liquidity and credit risks. Alshatti (2015) asserted that liquidity ratio (LTA) is crucial for banks to enhance their profit rate.

Based on GMM and fixed effect panel regression approaches, Yuksel et al. (2018) identified economic growth, loan amount, and non-interest income (NII) as the significant predictors of bank profitability among 13 post soviet countries from 1996 to 2016. Positive associations were noted for profitability with economic growth and NII. Increment in NII (e.g., commission and credit card fees) contributed to bank profitability and positively influenced banking financial performance.

According to Yüksel et al. (2018), bank profitability was positively affected by GDP, signifying higher GDP yielded higher profit gains in the post-Soviet nation, but the profit rate of banks was adversely linked to the loan to-GDP ratio. Hence, the post-Soviet nation should devise measures to enhance their NII, particularly when banks lend money to clients.

Studies had examined the link between bank profitability and economic progress. Boitan (2015) assessed the factors that affected the profitability of banks in the European Union (EU). The study revealed highly positive Granger causality for the relationship between bank profitability and GDP growth. Based on regression analysis, Albertazzi et al. (2016) and Ullah et al. (2016) reported the bank profit rate was affected by GDP growth. The same outcome was found by Al-Jafari and Alchami (2014) and Djalilov and Piesse (2016) using the GMM method. In Albania, Duraj and Moci (2015) found that macroeconomic variables were crucial indicators of bank profitability.

Using the GMM approach, Jabra et al. (2016) identified the positive impact of capital adequacy ratio (CAR) on the profitability of banks established across Brazil, Russia, India, China, and South Africa (BRICS) countries. In fact, similar outcomes were reported by Dao and Nguyen (2020), Nguyen and Nguyen (2020), Chowdhury (2015), and Noman et al. (2015) by employing a similar method for their investigations. By employing the regression analysis, Dawood (2014) and Khatun and Siddiqui (2016) reported that CAR emerged as the most significant factor that affected the profitability of the bank. On the contrary, Puspitasari et al. (2021) and Tangangisalu et al. (2020) claimed that CAR had an insignificant effect on Indonesian bank profitability.

In light of bank-specific variables, the NLP ratio (NLPR) is emphasized in the literature. According to Tan et al. (2016) and Djalilov and Piesse (2016), NLPR had adversely affected bank profitability by using the GMM method. Ariyadasa et al. (2016) reported the same by using a vector error correction model for Sri Lanka, while Nisar (2015) analyzed the determinants of bank profitability in Pakistan using the regression approach. In short, high NLP decreased bank profitability.

Caporale et al. (2017) assessed the effect of global financial crises on the banking industry across the MENA region for 2000-2012, along with the primary determinants of the profitability of both foreign and local banks. The empirical outcomes showed that local banks outperformed foreign banks during the crisis. Although the size was insignificant, net interest revenue and LTA exerted positive and negative impacts, respectively, on profitability. Essentially, GDP had a positive effect on local banks.

Many studies had assessed the determinants of bank profitability by using multiple techniques, including data envelopment, regression, Granger causality, and GMM approaches. The influential factors of bank profitability varied across countries. While most empirical analyses had focused on the US and EU, studies on the banking sector in the MENA region are in scarcity, particularly before the year 2012.

3. Data and Methodology

3.1. Data

Information regarding the internal features of 103 banks that operated from 2008 to 2016 in 10 MENA countries was gathered in this study from the Bankscope database to examine the influential factors of bank profitability. Meanwhile, the related macroeconomic variables were retrieved from World Bank and World Development Indicators (WDI) databases. The data is presented in thousand USD to enable comparability. Several banks and countries were excluded due to data unavailability. Table 1 lists the bank datasets obtained from 10 MENA countries.

Table 1: Total Number of Banks across the 10 Selected MENA Countries

3.2. Study Variables

Based on the literature review, the eight variables (two macro and six microeconomic variables) listed in Table 2 were selected for this study analysis. Due to the country-level analysis undertaken in this study, the two microeconomic variables were aggregated to the country level, thus reflected as macroeconomic variables.

Table 2: Details of the Study Variables (2008–2016)

Note: Inflation data were obtained from the World Bank website, GDP data were retrieved from WDI reports, and the other variables were gathered from the Bankscope database.

The descriptive statistics of the variables for the selected banks in 10 MENA countries for 2008–2016 is tabulated in Table 3. The variables were normally distributed with 11.49% of average profitability (based on ROE). Apparently, the banks across the 10 MENA countries performed poorer than banks established in developed countries. This finding is in agreement with that reported in prior studies (Farazi et al., 2011; Micco et al., 2007). The mean value of ROE (11.49%) indicated that the banks made an 11.49% return based on their equity. However, higher NLP (mean value: 6.4%) led to a loss in several banks, thus resulted in a lower return. All the assessed variables displayed low standard deviation values, except for L/D, size square, and ROE that appeared consistent in the dataset.

Table 3: Descriptive Statistics of Banks in 10 MENA Countries (2008–2016

Note: All figures are presented in percentage. ROE: Return on equity; CAR: Capital adequacy; Size square: Bank size; NPL: Credit risk; NII/II: Non-interest income/interest income; LTA: Log total asset square; GDP: Economic growth rate; INF: Annual consumer price inflation index; L/D: Loan over deposit.

Table 3 presents the details of the studied variables (ROE as the dependent variable, while micro and macroeconomic variables as the independent variables). The following reports the outcomes of macroeconomic variables (INF and GDP). The impact of INF on bank profitability relied on anticipation. Upon anticipation, the banks had easily adapted their interest rates based on the expected INF, signifying a positive effect on profit rate (Molyneux & Thornton, 1992; Islam & Nishiyama, 2016). On the contrary, any unanticipated alteration in the interest rate caused the two variables to share an adverse correlation (Noman et al., 2015; Ariyadasa et al., 2016).

Second, as GDP shows economic progress, its impact on bank profitability has to be positive (Djalilov & Piesse, 2016; Javaid, 2016; Saeed, 2014; Ahmad et al., 2016). As a crucial economic strength indicator in a country, higher GDP denotes increased economic activities and loan demand, which in turn, enhances lending activities that generate more income for banks (profit). Nisar (2015), for instance, discovered an insignificant correlation between bank profitability and GDP growth rate for the three GMM models. Meanwhile, INF exerted a significantly negative link with bank profitability at a 5% significance level. This finding supports the typical assumption that an increment in inflation decreases market profits, particularly when rising inflation is both incorporated in prices and unanticipated.

Turning to microeconomic variables, banks with big size should benefit from economic scale and share a positive association with profitability (Djalilov & Piesse, 2016; Saeed, 2014). In this study, bank size was measured using the square of natural logarithm for total assets (valued at 2014 USD) to determine the impact of bank size on profitability. The positive coefficient revealed that profitability increased with increment in size and vice versa. Another interesting variable is NII/II ratio that reflects bank-generated revenue, which should be positively linked with profitability (Albertazzi et al., 2016; Nisar, 2015).

The LTA refers to the ratio of loans to total assets. Staikouras et al. (2008) reported a positive relationship between loans and bank profitability. Nevertheless, the generous issue of bank loans displayed an adverse effect on bank profitability. Notably, the selection of varying control variables had a positive impact on bank profit rates (Valverde & Fernández, 2007). Next, in light of the loans to deposits ratio (LTD) (Regehr & Sengupta, 2016), the deposit amount signifies bank income source. The correlation between bank profitability and the loan amount was controversial and attributable to loan quality (Ahmad et al., 2016; Hanna, 2016; Regehr & Sengupta, 2016; Menicucci & Paolucci, 2016).

Another microeconomic variable assessed in this study, credit risk (NPL), was determined based on Impaired loans/ gross loans. Credit risk reflects the ratio of NLP to total loans or (NPLR is measured as NLP/total loans and advances of the bank, i, in time t (−/+)). Upon adhering to the indicator used by Nisar (2015) for his study in Pakistan, this present study employed the regression method and found that high NLP adversely affected bank profitability. This finding is similar to that reported by Ariyadasa et al. (2016) for the context of Sri Lanka. Using the GMM approach, Djalilov and Piesse (2016) found that bank profitability was influenced by property rights, credit risk, financial freedom, capital, inflation, size, GDP growth, and concentration. Syria Regression by Hanna (2016), as well as Ghana Regressions, by Opoku et al. (2016) and Laryea et al. (2016) found a negative NLP-profitability link.

As for the last microeconomic variable, CAR denotes bank capital versus risks (Equity/Total Assets). The CAR impact on bank profitability was uncertain. Several past studies reported a negative CAR-profitability link due to high capital (lower credit to clients). Meanwhile, some studies noted that high CAR enhanced the image of the bank, which in turn, enhanced its profitability (Molyneux & Thornton, 1992; Abreu & Mendes, 2001; Djalilov & Piesse, 2016; Saeed, 2014).

3.3. Model

In accordance with Ahmad et al. (2016), the model built in this study embedded ROE as the dependent variable to determine the influential factors of bank profitability in 10 MENA countries. The dataset was composed of cross-sectional and time-series aspects, namely panel or longitudinal data. The dataset comprised n cross-sectional units, denoted as i = 1, …, N, observed at T time period, t = 1, …., T. The estimated model was formulated based on Naude and Saayman (2005), as follows:

\(Y_{i d}=\alpha+\sum_{K=1}^{K} \beta_{k} X_{K a}+e_{I r}\)       (1)

Where, Y_it: ROAE; α_it: intercept; β_it: regression coefficient on i explanatory variable, and ε_it: error term assumed to be normally distributed with mean zero. It portrays a log-log multivariate model. Two random effect models were considered and the error term was adjusted for each bank. The independent and dependent variables used in this study are expressed in Equation 2:

\(\begin{aligned} \mathrm{ROE}_{a t}=& \alpha+\beta_{1} \frac{\mathrm{NII}}{\mathrm{II}_{\text {it }}}+\beta_{2} \mathrm{INF}_{u t}+\beta_{3} \mathrm{GDP}_{\mathrm{Ir}} \\ &+\beta_{4} \text { size square }_{k}+\beta_{5} \mathrm{LTA}_{h}+\beta_{6} \mathrm{LTD}_{u r} \\ &+\beta_{7} \mathrm{NLP}_{i t}+\beta_{\mathrm{s}} \mathrm{CAR}_{h}+e_{i t} \end{aligned}\)       (2)

Where, ROE: return on equity, NII/II: non-interest to interest income ratio, INF: inflation, GDP: economic growth rate, SIZE square: log assets square, LTD: loans to deposits ratio, LTA: liquidity ratio or total loans to total assets, NLP: credit risk, and CAR: capital adequacy ratio.

Notably, data can be analyzed using OLS, fixed effect, and random effect panel regression methods. Estimation of panel data models typically involves random or fixed effect models. The individual-specific effect becomes a random variable with the potential of being correlated and uncorrelated with explanatory variables in fixed and random effect models, respectively. The Hausman test via random effect model was employed in this study to identify the most suitable model.

4. Empirical Results

4.1. Correlation Matrix

This section presents the correlation coefficients that determined the collinearity of the variables. Multicollinearity is present when the correlation coefficient exceeds 0.70 (Wooldridge, 2015). Table 4 shows the correlations between independent and dependent variables, along with low multicollinearity probability. The results revealed that the highest positive correlation was between ROE (independent variable) and bank size, and this was followed by the positive link between NII/II and ROE.

Table 4: Correlations Among the Variables

Source: Authors’ calculation from the panel data set.

In light of the GDP growth ratio, although credit risk (NLP) was negatively linked with ROE, its association displayed higher strength than other variables. Meanwhile, INF was weakly and positively correlated to ROE. Referring to Table 4, CAR and ROE shared a weak and negative correlation, while The LTA-ROE link was positive. It is noteworthy to assert that the reported correlations are reflective of basic statistics based on the given datasets, but do not indicate the conclusive estimation outcomes.

4.2. Regression Results

The random effect panel regression was employed in this study to determine the influential factors of bank profitability in 10 selected MENA countries. In adherence to the Hausman test (Wooldridge version), the random effect model best suited this study, in comparison to the other two models. Table 5 tabulates the outcomes retrieved from the random effect panel regression method (Model 3), along with models built using OLS (Model 1) and fixed effect regression (Model 2) approaches for comparison purposes only.

The study results present the outcomes of new regulations imposed across the MENA region during the sample period as measures to overcome the consequences of the then global financial crisis. First, according to the report published by the Central bank, despite the stable asset quality; the overall economic condition was badly affected due to the financial crisis as action to delinquency was taken by MENA, including exposure to riskier assets abroad. After 2009, the banks in MENA have adopted prudent measures in monitoring and enhancing their risk management platforms, besides deploying control strategies to cover credit in the attempt to minimize the increasing slippages of the sector.

Based on Table 5, inflation displayed a significantly positive relationship with bank profitability in all three models, with statistically significant correlations in Models 1(1%) and 3(10%). This outcome is consistent with the expectation that increment in inflation improved business profit rates, especially when such correlation is anticipated for banks to adapt easily to their interest rate (as in the case of this present study). Thus, bank profitability was positively affected by inflation for this study context (Molyneux & Thornton, 1992; Islam & Nishiyama, 2016).

Table 5: Details of the Study Variables (2008–2016)

Note: Dependent variable refers to ROE: Return on equity or net income/total assets that represents bank profitability. Independent variables are CAR: Capital adequacy; Size: Bank size; NPL: Credit risk; NII/II: Non-interest income over interest income; LTA: Log total asset square; GDP: Economic growth rate; INF: Annual consumer price inflation index; and L/D: Loan over customer deposit. Table 2 lists the definition of the study variables. The model was estimated using a panel random effect estimator. F-test determined the model fitness. P-values are presented in parenthesis. ***; **; and * represent statistical significance at 1%; 5%; and 10% levels; respectively. Results of Models 1 and 2 are displayed for comparison with Model 3.

Economic growth had a significantly positive link with bank profitability. This was ascribed to the higher profits gained by banks during the expansion period. This result is supported by other studies as well (Djalilov & Piesse, 2016; Javaid, 2016; Saeed, 2014; Ahmad et al., 2016).

It was identified that NII to II ratio had been significant at 1% for all models, due to the positive coefficients for Models 1 until 3 (14.63, 19.48, and 18.33, respectively). This portrayed the significantly positive correlation between bank profitability and NII/II ratio. This finding is particularly important as it unravels the new strategy deployed by banks to reap profit (via e.g., credit card and commission fees), which resulted in enhanced financial performance. The finding is in line with that reported by Nisar (2015), Albertazzi et al. (2016), and Javaid (2016). Next, the significantly positive effect of bank size on bank profitability at a 1% significance level (see Table 5) indicated that increasing bank size led to increasing bank profitability in the MENA countries. This outcome is in agreement with past studies.

The statistically significant LTA-ROE link (Mamatzakis et al., 2005) reflected that higher LTA yielded more total revenues and II for the studied MENA banks. These results verified the finding that MENA banks preferred conventional lending activities, thus the inability of the banks to gain lucrative income from newer investment opportunities. This particular finding is in line with prior studies, such as that reported by Elfeituri and Vergos (2019) in the context of the competitive banking sector in MENA.

Finally, bank profitability was significantly and negatively affected by credit risk (NPL) at a 1% significance level with coefficients of (−0.326, −0.311, and −0.313, respectively) for Models 1 until 3. This signified that the profit rate in banks dropped by (32%, 31%, and 31%, respectively) for Models 1 until 3 as NLP increased by 1%. Based on this condition, one may conclude that credit risk was a major threat for banks in MENA from gaining high-profit rate, primarily due to the uncertain economic condition during the sample period (Lee et al., 2014a, 2014b; Pennathur et al., 2012). Despite being an excellent way to generate income among banks, excessive lending activities can exert an adverse impact on their financial performance due to non-selective borrowers with low credibility who may fail to re-pay their loans. This negatively affects bank probability, which is in agreement with Menicucci and Paolucci (2016) and Alhassan et al. (2016).

The R-squared values for Models 1 until 3 were satisfactory with the following results: 0.1290, 0.0975, and 0.1090 for Models 1, 2, and 3, respectively. The F-statistics appeared to be highly significant at a 1% significance level; reflecting the significance of the models presented in this study.

5. Conclusion

This study had identified the primary determinants of bank profitability in 10 MENA countries between 2008 and 2016. The random effect panel regression approach, in comparison to OLS and fixed effect regression approaches, offered comprehensive evidence for the context of the MENA region to interested parties, including practitioners and policymakers. Essentially, the key finding reported in this study refers to the significant effect of bank size on profitability, which is in line with prior studies for other contexts (Regehr & Sengupta, 2016; Menicucci & Paolucci, 2016). On top of that, the positive impact of LTA on ROE signified that higher lending activities generated more total revenues and II, further indicating the positive growth of GDP (Jeon et al., 2011).

The statistical analysis revealed the significance of the random effect model and the consistency of the outcomes. Although increment in NII/II ratio enhanced bank profitability, the banks across the MENA region should not rely solely on the income generate from II. The banks should venture into other income-generating initiatives, such as fees charged for commission and credit card services. New channels of income are bound to benefit these banks in the long run. Apart from NII/ II, economic/GDP growth emerged as a significant factor for all three estimation regression approaches. As banks face more risks during the recession period, but reap more profits during the expansion period; their lending activities should be selective upon weighing the consequences of the recession period.

The NLPR-ROE adverse association recorded in this study for MENA banks was ascribed to low-quality loans. Uncontrolled lending activities without inadequate risk assessment would eventually rope in low credibility clients who might face difficulties in repaying the loans. Such a scenario would eventually affect banking performance and profitability negatively. Therefore, it is integral for the regulatory institutions to keep their NLPR under control to hinder unpleasant consequences.