Non-Performing Loan (NPL) Scenario of Banking Industry in Bangladesh During Pandemic: Do the Changes Blessing or Curse?

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Non-Performing Loan (NPL) Scenario of Banking Industry in Bangladesh During Pandemic: Do the Changes Blessing or Curse?

Ishter Mahal

Associate Professor

Department of Accounting and Information Systems University of Dhaka

Benazir Rahman

Assistant Professor Department of Finance and Banking Northern University Bangladesh

Arif Ahmed

Assistant Professor

Department of Accounting & Information Systems Jatiya Kabi Kazi Nazrul Islam University

Abstract

Banking industry mainly deals with other’s money. COVID-19 has spread out all over the world so rapidly and created pandemic situation which affected all the socio-economic and financial system worldwide. This pandemic affected the banking industry in both direct and indirect way. Banks faced losses in their field as well as these banks have to back their clients as well. The study focuses on the impact of pandemic over the non-performing loans (NPL) in banking industry of Bangladesh. The prime objective of the research is to show the influences COVID-19 made in NPL trends and the changes faced due to this pandemic. Basically, secondary data have been used. A comparative analysis has been done to show the situation before and during pandemic. Due to the limitation of not getting data of the year 2021 data of 2019 and 2020 was used as post-pandemic period and year 2017 and 2018 has been considered as pre-pandemic scenario. Different statistical tools like regression analysis, one-way ANOVA and so on has been used to justify the data. The study reveals that NPL trend doesn’t change chronically but changes are visible. Subsidies given by Govt., amended policies of Bangladesh bank are the reasons behind it. The paper is useful for the bankers and academia.

Keywords: Classified loan, Pandemic, ROE, Banking Industry, Loan rescheduling.

1.0 Introduction

According to the guidelines of Banking Regulation & Policy Department (BRPD), Bangladesh Bank, a classified loan means that portion of total loan disbursed by a bank for which the debtor has not made any scheduled payment at least for 90 days. On the other hand, classified loan is an umbrella term that is categorized into three distinct

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THE COST AND MANAGEMENT

ISSN 1817-5090, VOLUME-49, NUMBER-05, SEPTEMBER-OCTOBER 2021

categories namely, substandard, doubtful, and bad loss based on the length of their remaining overdue.

Loan default in the Banking sectors of Bangladesh has already become a culture that is a big threat to the performance and sustenance of a bank, and it is too dangerous a thing that can lead to a substantial financial crisis for a country (Ahmad and Bashir, 2013).

Curving default loan has become one of the cardinal issues for the regulators of the banking sector in Bangladesh. Though, in a developing country like ours, ensuring a good loan behooves the bank itself, often, it doesn’t obtain for various circumstances beyond control. Whereas most of the default loans in Bangladesh are found to occur due to the unscrupulous behavior of the borrowers, regulators and the lenders cannot eschew their respective responsibilities in this regard. For example, the inefficiency of management in calculating the creditworthiness of a client is one of the significant drivers of non-performing loans in Bangladesh. On the other hand, an unwillingness of the client or the subsequent failure in business may lead the borrower to default. There are many more reasons, and a lot can be said about what should be done, what was done and what should have been done.

During the pandemic Covid-19, induced by the rogue virus Corona, starting almost three years back, businesses all over the world faced an unprecedented challenge to survive let alone earning profit. This downturn, on the other hand, has significantly added fuel to the fire of default loan internationally. Amid this period, default loan ballooned internationally (Ari et. al. 2020).

To help the businesses many countries in the world have issued some lenient policies regarding loan repayment, rescheduling, reclassification, and loan restructuring (Guo et. al 2021). Bangladesh is no exception from that. The highest regulator of the country’s banking sector--Bangladesh Bank in line with the recommendations made by the ministry of finance, has issued several circulars (BRPD) to guide the financial entities of the country during pandemic.

Most of those are on loan rescheduling, restructuring and classification.

To rein the loan default during the pandemic, these intermittent policies by the Bangladesh Bank are supposed to backfire in near future. These rescheduled amounts are accumulating significantly, and no one

knows how much of these have already turned into default loan, due to the absence of regular default policy. In near future, when loan policies will come as it was before, loan default situation will exacerbate severely, without any doubt.

This research is quite unique as it is rare to find research which explains solely non-performing loans scenario of banking industry in Bangladesh during COVID-19 pandemic. In the following sections, this research discusses different related literatures followed by research objectives, sample and data sources, and research methodology. Later, it discusses the general scenario of banking industry considering ROE and NPL with one way ANOVA and multiple regression analysis for both pre pandemic and post pandemic scenario. Then, it discusses the findings of statistical models and draws the conclusion mentioning some limitations of this paper what can be considered as future research purposes.

2.0 Literature Review

Bari (2020) studied financial difficulty of banking management during pandemic in Bangladesh by analyzing the financial scenarios of household and businesses and their impact on banking transactions.

Both the metropolitan cities were taken into consideration. Savings rating operations and performance of private business banks have exceeded those of state-owned industrial banks, according to findings. Small and large businesses are all shut down or closed for an undetermined amount of time. As consumers and businesses grapple with a liquidity crisis, fears are growing. The primary elements that were causing the credit score issues (i.e., non- performing loans) were thoroughly examined.

Corporate governance, savings management, financial savings legislation, and the degree of political meddling were all highlighted as contributing factors.

The best course of action is for Bangladesh Bank to orchestrate a healthy economic growth based on a four-percentage-point repo rate and a lending rate of 5-8 percent.

The Bangladeshi banking system is facing massive financial losses, mounting non-performing loans, individual investment, and diminishing operating profits during this critical period of lockdown. The

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ability of bank workers to carry out their daily tasks is severely harmed. They go about their days in a state of terror. Research has been carried out to determine the impact of the Covid-19 pandemic on Bangladesh’s financial industry, as well as the risk involved in the banking sector during the pandemic. To achieve the study’s objectives, a qualitative research approach was used. The focus group method and observation method were utilized to obtain data. Twelve Bangladeshi bank employees participated in a focus group discussion. Six of the 12 bank employees worked for state-owned banks, while the other six worked for private banks and following factors were considered. (Kumar et al. 2021)

Source: Kumar et al., 2021

COVID-19 pandemic, which broke out in the first quarter of 2020, caused a slew of unprecedented emergency measures, including travel bans, mandatory closure of non-essential businesses, gathering restrictions, and required home-based labor and Europe is not an exception. But it has started to spread its damages from the 3rd quarter of 2019 worldwide. Due to COVID-19, several borrowers’ revenue flow was drastically reduced or completely stopped. Borrower relief initiatives have been quickly implemented by policymakers. In Europe and Central Asia (ECA), these programs have mostly taken the form of temporary payment moratoria, with banks making the final decisions on which borrowers qualify. To flatten the bankruptcy curve, short-term legal measures are paired with long-term legal measures. While these measures have kept aggregate non-performing loan (NPL) ratios stable, policymakers and bankers anticipate that rising levels of borrower distress will inevitably translate into new pressures on asset quality in the banking sector, which will show up in banks’ earnings, capital, and financial statements. The following basic models were suggested.

Source: handbook for MSME NPL Management and Workout.

Covid-19 Pandemic

Lockdown

Increase NPLs

Decrease Individual Investment Decrease

Banks Gross Profits Hamper

Regular Banking Activities

Capital Shortfall

Increase Risk in Banking

Sector

A Schematic overview of loan restructuring measures

Loan restructuring measures

Short-term, temporary

Reduced payments Interest Only

Moratorium Extension of maturity dates

Capitaliztion of deferred debt payments

Conditional debt forgiveness Interest rate

reduction Rescheduling with

NPV reduction Sle by owner Loan spletting Note sale Long-term, temporary

Borrower is facing deeper-roated solvency problems Borrower is facing short-term

liquidity stress

NPV neutral Material NPV

reduction

Possible additional measures -Debt-to-assest swaps -Debt-to-equity swaps -Debt consolidation -Other alterations of contracts -Additional Security

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Žunić et al. (2021) focused on the factors that influence the movement of non-performing loans in Bosnia and Herzegovina’s banking industry, as well as the impact of the COVID-19 pandemic. Non-performing loans, GDP, loan loss provision, and dummy variable COVID-19 were used as variables in the analysis and is found that the variable COVID-19 has a delayed effect on NPLs.

Rachmadi and Suyono (2021) found that credit restructuring plays a significant negative role in NPL of Banks along with reducing interest rates due to changes in accounting treatment particularly in the recognition of restructured credit interest income. According to them, the implementation of credit restructuring is carried out as an effort to face the effects of the COVID-19 pandemic, which affected all sectors, especially the economic sector in the MSME business sector, which resulted in banking performance on several items including profit, credit quality and interest income as a source of main income.

Chen et. al. (2021) in their study titled “The dynamics of non-performing loans during banking crises: A new database with post-COVID-19 implications” found that the impact of Covid-19 induced pandemic to the non-performing loans is very similar to the impact of other crises like the one of 1990, but the find heterogeneity in the pace of NPLs related resolution during the period. They documented how high and unresolved NPLs deepen post-crisis recessions and use a machine learning approach to establish pre-crisis predictors of NPL problems. According to them, these predictors—a set of weak macroeconomic, institutional, corporate, and banking sector conditions—help shed light on post-COVID-19 NPL vulnerabilities.

Ari et al. (2021) in their paper titled “COVID-19 and Non-Performing Loans: Lessons from past Crises”

found that during the crisis resulting from COVID -19, non-performing loans increased. They asserted on the learning that the banking sector acquired from the past crises like the ones of 1990 and 2008.

According to them, dealing with NPLs is critical to economic recovery. Compared with the 2008 crisis, some factors are conducive to NPL resolution this time: banks have higher capital, the forward-looking IFRS 9 accounting standards can help NPL recognition, and the COVID-19 crisis was not preceded by a

credit boom. However, other factors could make NPL resolution more challenging: government debt is substantially higher, banks are less profitable, and corporate balance sheets are often weak

Gourinchas et al. (2020) estimated the impact of COVID-19 on business failures for small and medium-sized enterprises (SMEs) using firm-level data in seventeen countries. They averred that absent government support, the failure rate of SMEs would have increased by 9.1 percentage points, representing 4.6 percent of private-sector employment. According to them, resulting non-performing loans are modest, decreasing the risk-weighted common equity Tier-1 capital ratio from 14.1 to 12 percent. Government support limited to “at-risk” firms would have low fiscal costs (0.8% of GDP). Less targeted policies such as government-guaranteed loans are similarly effective, but substantially more expensive, with disbursed funds representing up to 5.8% of GDP.

Haynes et al. (2021) discovered that the COVID-19 crisis is a significant and exogenous shock to the EU corporate sector, with implications for the operations and funding of many businesses. They compared key indicators for the global financial crisis (GFC) and the current situation and assess implications for the policy response. They find that while many policy actions are taken in response to the GFC remain valid, the nature of COVID-19 suggests a more tailored response is appropriate, with support focused on sectors most directly affected and corporates whose continuation value exceeds their liquidation value.

In similar research but performed in different settings titled “Nonperforming loans - new risks and policies?

NPL resolution after COVID-19: Main differences to previous crises” Kasinger et. al. (2021) discusses policy implications of a potential surge in NPLs due to COVID-19. The study provides an empirical assessment of potential scenarios and draws lessons from previous crises for effective NPL treatment. Their paper highlights the importance of early and realistic assessment of loan losses to avoid adverse incentives for banks. They asserted that secondary loan markets would help in this process and further facilitate bank resolution as laid down in the BRRD, which should be upheld even in extreme scenarios. This paper was prepared by the Economic Governance Support Unit (EGOV) at the request of the Committee on Economic and Monetary Affairs (ECON).

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Bodellini and Lintner (2020) in their research commented that the economic crisis provoked by the COVID-19 pandemic is soon expected to also hit the banking and financial sector, mainly through a massive increase of non-performing loans resulting from bank borrowers’ inability to repay their debts.

Regulators and supervisors have already put in place several measures in response to the current situation mainly aimed to facilitate banks continued lending. It is still difficult to forecast whether bank capital will be sufficient to absorb the shock if the risk of the non- performing loan materializes. According to them, having effective bank crisis management regimes in place, which may also rely on public intervention, will be key to limiting spill-over effects that could pave the way to another global financial crisis. The current public interest test leads to the application of different conditions for access to resolution financing and the provision of public support, depending on whether the BRRD or the banking state aid framework under national insolvency rules is followed. For bank crisis management regimes to work properly, more flexibility in financing by deposit guarantee schemes beyond pay-outs or simple loss contributions under resolution will be required to facilitate transfer tools.

Barua and Barua (2021) in a study titled “COVID-19 implications for banks: evidence from an emerging economy” commented that the COVID-19 pandemic is damaging economies across the world, including financial markets and institutions in all possible dimensions. For banks the pandemic generates multifaceted crises, mostly through increases in default rates. This is likely to be worse in developing economies with poor financial market architecture.

Their paper utilized Bangladesh as a case study of an emerging economy and examines the possible impacts of the pandemic on the country’s banking sector. They intimate that Bangladesh’s banking sector already has a high level of non-performing loans (NPLs) and the pandemic is likely to worsen the situation. Using a state-designed stress testing model, their study estimates the impacts of the COVID-19 pandemic on three dimensions—firm value, capital adequacy, and interest income—under different NPL shock scenarios. They find that all banks are likely to see a fall in risk-weighted asset values, capital adequacy ratios, and interest income at the individual bank and sectoral levels. However, their estimates show that larger banks are relatively more vulnerable. The decline in

all three dimensions will increase disproportionately if NPL shocks become larger. Findings further show that a 10% NPL shock could force capital adequacy of all banks to go below the minimum BASEL-III requirement, while a shock of 13% or more could turn it to zero or negative at the sectoral level.

A similar study conducted by Lalon (2020) suggested that the whole globe be going under a devastating threat of economic depression amid the impact of the COVID-19 pandemic. Almost no country can deny the fact propelling to the economic ramification of this diseases suggesting a confirmed apropos plan to recuperate any unavoidable circumstance in the forthcoming economic arena. Bangladesh with no exception also capitulates under a significant threat of economic disparity navigating a colossal crisis during and after this epidemic. His study attempts to reveal what possible impacts are causing this economic crisis for Bangladesh and how government along with all other stakeholders will respond to sustain socio- economic developments achieved during the recent fiscal years despite being submerged by the depressing mode of major economic indicators such as inverse trade growth, vigorous revenue deficit, mounting non- performing loan, falling private sector investment, volatility of market interest rate, capital market unrest and imminent horrid of global economic recession.

Bari (2020) pointed out that the savings rating operations and performances of private business.

Banks have outperformed these of state-owned industrial banks during COVID-19. The mortgage disbursement techniques of state-owned industrial banks have been now no longer efficient ample to reap the required recuperation target. Further, he found that the state-owned business banks are increased likely to be affected via using each and every one of the contributory factors a long way larger adversely than non-public business banks. Effective use of company governance, maintaining transparency and accountability in all respect, environment- friendly financial savings risk management, bettering managerial efficiency, profitable privatization, lessening political interference, and adapting contemporary technological changes, would possibly also enhance the well-known personal loan trouble scenario of state-owned industrial banking region of Bangladesh.

Rahman et al. (2020) commented that the Banking sector of Bangladesh had been struggling with poor performance before the COVID-19 situation.

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According to them, the reasons behind it includes non-performing loans, declining margins in a capped interest rate regime, deteriorations in various efficiency indicators, government-directed restructuring of loans. They found that social distancing and government economic support significantly affect the bank’s liquidity.

3.0 Research Question

After finding the literature gap the following research questions are derived:

Q1: does pandemic affect the NPL status significantly?

Q2: what are the differences between NPL scenarios before and during pandemic?

Q3: does the impact differs for the different categories of bank?

4.0 Methodology

The study is heavily dependent on secondary data as it dealt with the figures and amounts related to classified loans to justify the scenario.

4.1 Sample Size And Selection

For the study we covered almost 50% banks of different categories of banking industry in Bangladesh.

Table-01: Number of selected banks Banking categories Total number of banks in

Bangladesh Taken as

sample Percentage of population covered

State-owned Bank (SCB) 6 3 50%

Private owned Islamic (IB) 8 5 62.50%

Private owned Conventional (PCB) 41 (Islamic banks are excluded) 26 63.41%

The following banks have been taken from these three banking sectors.

Table-02: list of selected banks

Bank Category Selected banks

State-Owned • Agrani Bank Limited

• Bangladesh Development Bank Limited

• Janata Bank Ltd.

Private Owned Islamic • Al-Arafah Islami Bank

• EXIM

• SIBL

• First security Islami Bank

• Union Bank

Private Owned Conventional • AB Bank Limited • Pubali Bank

• Bank Asia Limited • Prime Bank

• Brac Bank Limited • Uttara Bank

• City Bank Limited • Standard Bank

• Commerce Bank Limited • DBBL

• Dhaka Bank Limited • Eastern Bank

• NRB Bank Limited • IFIC

• NRBC Bank Limited • Jamuna Bank

• One Bank • Marcantile Bank

• Premier Bank • Mutual Trust bank

• Trust Bank • National bank

• City Bank • Modhumoti bank

• SEBL • NCC

For the study data from year 2017-2020 have been collected to compare the scenario of pre and post pandemic time.

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4.2 Data Collection

The research is based on secondary data as ratios and figures from banking industry had been used to prove the impact of COVID 19 over the classified loans of the industry. Secondary data have been collected from annual reports and websites of selected banks. Different websites were also used to justify the research.

4.3 Data Instrumentation

Comparison of pre and post pandemic impact on non-performing loan scenario has done by different graphs and One-way ANOVA. MS Excel and SPSS 16.0 software was used to compute these statistical values

For the one-way ANOVA categories of banks has considered as factor and NPL ratio has considered as dependent variable.

For the data analysis average data of each bank for the year 2017 and 2018 has considered as pre-pandemic situation and average data of each bank for the year 2019 and 2020 has considered as post-pandemic situation.

Regression analysis for Return on Equity (ROE) (dependent Variable), total loans and advances (independent variable) and Non-performing loan ratio (NPLR) (independent variable) were done for both periods to compare the scenario.

For multiple regression analysis:

H1.1: The impact of NPLR and total loans and advances are strong over ROE before pandemic.

H1.2: The impact of NPLR and total loans and advances are strong over ROE during pandemic.

For one-way Anova:

H2.1: The impact of NPLR is same for all categories of banks over ROE before pandemic.

H2.2: The impact of NPLR is same for all categories of banks over ROE during pandemic.

5.0 Analysis And Findings:

General scenario of Bangladesh

Banking industry is one of the prime industries of Bangladesh. Like other industries this industry also faced difficulties during pandemic. Banking industry deals with other’s money and when pandemic disrupted the many industries it’s directly and indirectly affected banking industry as well.

Source: BRPD, BB

2 0 1 7 pandemic Pre

2 0 1 8

2 0 1 9 Post

pandemic

2 0 2 0

Regression analysis for Return on Equity (ROE) (dependent Variable), total loans and advances (independent variable) and Non-performing loan ratio (NPLR) (independent variable) were done for both periods to compare the scenario.

For multiple regression analysis:

H1.1: The impact of NPLR and total loans and advances are strong over ROE before pandemic.

H1.2: The impact of NPLR and total loans and advances are strong over ROE during pandemic.

For one-way Anova:

H2.1: The impact of NPLR is same for all categories of banks over ROE before pandemic.

H2.2: The impact of NPLR is same for all categories of banks over ROE during pandemic.

5. ANALYSIS AND FINDINGS:

5.1. GENERAL SCENARIO OF BANGLADESH

Banking industry is one of the prime industries of Bangladesh. Like other industries this industry also faced difficulties during pandemic. Banking industry deals with other’s money and when pandemic disrupted the many industries it’s directly and indirectly affected banking industry as well.

Source: BRPD, BB

2017 2018

pandemicPre

2019 2020

Post Pandemic

-40.00 -30.00 -20.00 -10.00 0.00 10.00 20.00 30.00

2011 2012 2013 2014 2015 2016 2017 2018 2019 End June

2020 ROE

Fig. 01: ROE for different types of banks

SCBs SBs PCBs FCBs Total

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The overall scenario showed that Due to the downward trend of state-owned and special banks the banking industry faced lower and sometimes inverse industry average. But during pandemic the ROE of SCB has increased.

Table-03: Ratio of gross NPLs to Total Loans by Type of Banks

(In percent) Bank types 2011 2012 2013 2014 2015 2016 2017 2018 2019 End

June 2020 State-owned Commercial Banks 11.3 23.9 19.8 22.2 21.5 25.0 26.5 30.0 23.9 22.7 Specialized Banks 24.6 26.8 26.8 32.8 23.2 26.0 23.4 19.5 15.1 15.9

Private Commercial Banks 2.9 4.6 4.5 4.9 4.9 4.6 4.9 5.5 5.8 5.9

Foreign Commercial Banks 3.0 3.5 5.5 7.3 7.8 9.6 7.0 6.5 5.7 5.5

Total 6.1 10.0 8.9 9.7 8.8 9.2 9.3 10.3 9.3 9.2

The industry average for gross NPL to total loans shows that it has increasing trend since 2012 but the changes

during pandemic was not very chronic.

The trend of NPL amount and provision requirement tell the similar story. But the prime concern of the study is the changes before and during pandemic is not so significant in case of NPL scenario.

5.2 One-Way Anova 5.2.2. Pre-Pandemic:

Table-04: ANOVA for Pre-pandemic scenario NPLR

Sum of Squares df Mean Square F Sig.

Between Groups 1126.152 2 563.076 9.203 .001

Within Groups 2019.165 33 61.187

Total 3145.318 35

p-value or significance level is almost 0 here, which means that the standard deviations of the populations are different and before pandemic different categories of banks achieved different returns and banks’ performance level has also different.

The trend of NPL amount and provision requirement tell the similar story. But the prime concern of the study is the changes before and during pandemic is not so significant in case of NPL scenario.

5.2. ONE-WAY ANOVA 5.2.2. PRE-PANDEMIC:

Table-04: ANOVA for Pre-pandemic scenario NPLR

Sum of

Squares df Mean

Square F Sig.

Between

Groups 1126.152 2 563.076 9.203 .001

Within Groups 2019.165 33 61.187

Total 3145.318 35

p-value or significance level is almost 0 here, which means that the standard deviations of the populations are different and before pandemic different categories of banks achieved different returns and banks’ performance level has also different.

5.2.3. POST-PANDEMIC:

Table-05: ANOVA for post pandemic scenario NPLR

Sum of

Squares df Mean

Square F Sig.

0.0 100.0 200.0 300.0 400.0 500.0 600.0

2011 2012 2013 2014 2015 2016 2017 2018 2019 End june 2020

Fig.-02:Amount of NPLs by Type of Bank

SCB s SBs PCBs FCBs

-200.0200.0400.0600.0800.00.0 1000.0 1200.0

2011 2012 2013 2014 2015 2016 2017 2018 2019 End june 2020

Fig.-03: Requred Provision and Provivon Mantained-all Banks

Amount of NPLs Required Provision Provision maintained Excess(+)/ Shortfall(-)

Provision maintenance ration (%)

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5.2.3. Post-Pandemic:

Table-05: ANOVA for post pandemic scenario NPLR

Sum of Squares df Mean Square F Sig.

Between Groups 1221.108 2 610.554 4.470 .019

Within Groups 4507.918 33 136.604

Total 5729.026 35

Here p-value or significance level is almost 0.019, which means that the standard deviations of the populations are different, but differences are not highly significant. It shows due the pandemic some changes occurred, and the density of deviation changed. pandemic different categories of banks achieved different returns and banks’

performance level has also insignificantly different. Govt. banks got the back up in some extent as well.

5.3 Multiple Regression Analysis

Table-06: Multiple-regression analysis for pre and post pandemic scenario Pre-pandemic

Independent variables Dependent variable N R2 F-test sig D-test beta Sig.

NPLR ROE 36 26.5% 11.287 0.004 2.25 -1.24 .006

Total loans and advances 9.87 .000

Post-Pandemic

NPLR ROE 36 28% 0.166 0.753 2.061 -0.17 .322

Total loans and advances 8.56 .004

The regression analysis shows that before pandemic the relation was significantly strong but during pandemic the relationship becomes insignificant. The reason behind the fact is Govt. has changed or amended different policies to help banking industry like decreasing cash reserve ratio, changes in rescheduling policies on default loans and reporting of classified loans and so on.

6.0 Discussions

The pandemic has changed many things. The COVID 19 has affected the economic and financial systems all over the world. Banking industry is not an exception. During this pandemic many businesses got shut and big companies faced financial distress as well. Fortunately, Bangladesh bank has amended some features and conditions of loan restructuring and in case of classifying loans. According to the BRPD no. 5(May. 2019) some clauses have been amended like as borrowers who have overdue during the whole 2020, they got the privileged of not being considered as defaulters and banks got relaxation of not counting those loans as classified loans.

Also, banks need to rethink on classified loans of some specific sectors more precisely productive sectors like trading, shipping, steel and iron and the borrowers of the following sectors will be favored by giving opportunity to submit Rescheduling loan Exit under Special RSDL under BRPD Circular No.-05/2019 and one time Exit Special Exit under BRPD Circular No.-05/2019 and report under CL-4(Term loan). Different stimulus packages are provided by the banks as per the instructions of Bangladesh Bank like banks paid salaries to the employees on behalf of their regular clients from garments and export industry in 0% interest rate and clients got chance to pay it later. Borrowers are allowed to pay only 4% of interest whereas Bangladesh bank subsidized 5% interest expenses in case of some industries.

The results of this research also revealed that despite having so many crisis NPL ratio of banks have not changed significantly. The reason is the backup and relaxation given by the Bangladesh bank. But the reality is banks are facing huge financial issues as they are even backing up some of their clients during this pandemic and collection of loan payment is poor. Yet due to the new circular NPL ratios didn’t hamper drastically. But their return on equity got affected.

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7.0 Conclusion

The study concluded that though the NPL ratios didn’t change chronically but the banking industry faced a lot of crises during this COVID-19 period and it influenced their profitability and work efficiency as well. The study has some limitations like data of the year 2021 is not available which creates a new room for further research.

Due to pandemic face-to face interviews were avoided. In a nutshell, it can be said that COVID-19 drew a line in every industry and banks are not exception. But due to Central bank’s backup and amended loan rescheduling policies NPL ratios are not affected so badly but the overall financial health of banks in Bangladesh have adversely influenced by this pandemic.

Reference

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Ari, A., Chen, S. and Ratnovski, L., 2020. COVID-19 and non-performing loans: lessons from past crises. Available at SSRN 3632272.

Bari, M.S., 2020. Financial difficulty of Banking management during COVID 19 in Bangladesh. International Journal of Finance Research, 1(2), pp.

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Barua, B. and Barua, S., 2021. COVID-19 implications for banks: evidence from an emerging economy. SN Business & Economics, 1(1), pp.1-28.

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Gourinchas, P.O., Kalemli-Özcan, Ṣ., Penciakova, V. and Sander, N., 2020. Covid-19 and SME failures. National Bureau of Economic Research, Cambridge, Working Paper no. 27877.

Guo, X., Guo, C., Zhou, J., Wang, G. and Yang, H., “Research on Early Warning of Non-performing Loans of Small and Medium-sized Micro- enterprises Under the Background of COVID-19 -- Taking XX Branch of N Bank as an Example” (2021). WHICEB 2021 Proceedings. 52.

Haynes, J., Hope, P. and Talbot, H., 2021. Non-performing Loans–New Risks and Policies. NPL Resolution after COVID-19: Main Differences to Previous Crises. Study Prepared by the Economic Governance Support Unit (EGOV) at the Request of the Committee on Economic and Monetary Affairs (ECON)(March).

Kasinger, J., Krahnen, J.P., Ongena, S., Pelizzon, L., Schmeling, M. and Wahrenburg, M., 2021. Non-performing loans-new risks and policies? NPL resolution after COVID-19: Main differences to previous crises (No. 84). SAFE White Paper.

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APPENDIX:

Appendix-01: Pre-pandemic data

SOB Before pandemic scenario (2017 & 2018)

Bank's name ROE NPL Ratio Total loans and advances

Agrani Bank Limited 9.54% 17.56% 357435

BDBL 3.51% 46.30% 18,627

Janata bank 2.85% 25.13% 493707.160

Basic bank 3.16% 4.13% 598632.809

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Isl. Bank Before pandemic scenario (2017 & 2018)

Al Arafah 12.27% 4.45% 248889.5

EXIM 10.27% 5.22% 279419.75

SIBL 10.16% 0.03% 285036.075

FSIBL 19.92% 3.11% 214042.576

UNION BANK 2.45% 0.77% 2910869.64

IBBL 10.51% 3.85% 115934.115

Conventional Bank Before pandemic scenario (2017 & 2018)

AB BANK 0.11% 20.11% 235358.5

Bank Asia 7.99% 3.93% 206,061

Brac Bank 20.70% 3.33% 220283.5

City bank 12.05% 5.35% 213993.5

Bangladesh Commerce Bank limited -0.46% 33.89% 20,716

Dhaka Bank 8.70% 5.49% 167321.5

NRB Bank 3.60% 3.09% 27,697

ONE bank 12.57% 6.18% 184,651,109,591

Prime bank 6.42% 5.81% 202,066,123,754

Trust bank 15.00% 5.63% 191,019,380

SEBL 6.815 5.93% 250,994,175

Premier bank 15.155 4.34% 155,065,125,242

Pubali Bank 5.68 7.07% 255,225,000

Uttara Bank 11.43% 6.54% 112,025,344,055

standard bank 8.86% 7.93% 137,144,500,000

DBBL 7.02% 4.39% 219405660723.50

Eastern bank 1.80% 2.43% 196666775789.00

IFIC 9.88% 6.28% 189746925000.00

Jamuna bank 7.84% 3.90% 153827890352.50

Mercantile bank 17.05% 4.31% 211945665000.00

Mutual Trust 16.09% 4.85% 155876056455.50

National bank 10.95% 10.07% 284898017290.50

Modhumoti Bank 14.87% 1.06% 32878474260

NCC 10.47% 5.80% 160250315336.00

NRBC 14.60% 2.70% 45539148136

United commerce 8.85% 6.79% 277,837,410,000.00

Appendix-02: Post-pandemic data

SOB After pandemic scenario (2019 & 2020)

Bank's name ROE NPL Ratio Total Loans and Advances

Agrani Bank Limited 2.78% 13.36% 489440.838

BDBL 0.40% 33.17% 20,984

Janata bank 0.43% 24.63% 607113.27

Basic bank 4.77% 28.25% 757378.37

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40

Isl. Bank after pandemic scenario (2019 & 2020)

Al Arafah 10.08% 4.31% 298553.5

EXIM 8.86% 4.08% 392919.662

SIBL 9% 4.16% 304213.741

FSIBL 26.84% 57.60% 176276.654

UNION BANK 4.26% 2.31% 129459.574

IBBL 8.37% 3.62% 10352878.822

Conventional Bank after pandemic scenario (2019 & 2020)

AB bank 1.20% 17.54% 265671

Bank Asia 10.21% 4.24% 235,971

Brac Bank 13.09% 3.46% 268577

City bank 12.35% 4.90% 257573

Bangladesh Commerce Bank limited -2.83% 46.36% 22,778

Dhaka Bank 10.28% 3.94% 197147.5

NRB Bank 0.73% 3.93% 38890.5

One bank 9.04% 6.66% 217,784,685,811

Prime Bank 6.12 0.0406 223,177,500

Trust bank 12.89 0.05 2,118,054,621

SEBL 7.85 3.99% 309,502,210

Premier bank 15.155 4.64% 225,649,965,242

Pubali bank 8.485 3.56% 301,307,000

Uttara Bank 12.11% 7.08% 129,262,335,449

standard bank 7.85% 5.11% 161511147248.00

DBBL 6.27% 3.27% 264811308117.50

Eastern bank 15.78% 3.04% 230497500000.00

IFIC 6.40% 4.66% 244619394881.00

Jamuna bank 8.28% 3.33% 169968605000.00

Mercantile bank 10.52% 4.79% 242942420000.00

Mutual Trust 7.43% 5.00% 196808518965.50

National bank 7.92% 10.15% 387326582121.50

Modhumoti Bank 17.01% 1.88% 1924742725873.00

NCC 11.31% 4.90% 178597994724.00

NRBC 14.99% 3.07% 68473494284

United commerce 8.63% 3.09% 337205850000.00