Bank non-performing loans research around the world

Ozili, Peterson K. Article Bank non-performing loans research around the world Asian Journal of Economics and Banking (AJEB) Provided in Cooperation with: Ho Chi Minh University of Banking (HUB), Ho Chi Minh City Suggested Citation: Ozili, Peterson K. (2025) : Bank non-performing loans research around the world, Asian Journal of Economics and Banking (AJEB), ISSN 2633-7991, Emerald, Leeds, Vol. 9, Iss. 3, pp. 437-462, https://doi.org/10.1108/AJEB-09-2024-0103 This Version is available at: https://hdl.handle.net/10419/334154 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Ozili Central Bank of Nigeria, Abuja, Nigeria Abstract Purpose – This article presents a literature review of bank non-performing loans (NPLs) research around the world and suggests directions for future research. Design/methodology/approach – The study used the thematic and bibliometric literature review methodologies to present a review of the recent NPL literature that have emerged since 2020. Findings – Significant NPL research has emerged from the European, Asian and African regions, while fewer research studies have emerged from the Asia–Pacific, North America, Latin America and Caribbean regions as well as from the South Asian Association for Regional Cooperation and Organization for Economic Cooperation and Development countries. The new NPL determinants in the recent literature are corporate governance, fintech, financial inclusion, country risks, regulatory quality, political risks, shadow banking activity, the COVID-19 pandemic, public and/or external debt, country risks, real house prices and the independence of the central bank. The common regional NPL determinants are corruption, gross domestic product (GDP), debt, loan growth, inflation, capital adequacy ratio, lending rate, competition, the regulatory environment and GDP growth. The common theories used in the recent literature to explain the behavior of NPL are agency theory, stakeholder theory, information asymmetry theory and moral hazard theory, while the common empirical methodologies used are the panel regression and system generalized method of moments regression methods. Practical implications – Financial regulators, bank supervisors and banking scholars should pay attention to the new emerging determinants of NPL. They should also understand the effect of NPL on financial and/or banking stability so that safeguards can be put in place to minimize the adverse effect of NPLs. More research is needed to provide insights into this area. Originality/value – To date, no study has presented an overview of the post-2020 NPL literature to identify the new determinants and effects of NPL across several contexts and regions. Keywords Banks, NPL, Non-performing loans, Research, Determinants, Literature review, World Paper type Literature review 1. Introduction Bank non-performing loan (NPL) (NPL) is a topic of great importance in the banking and finance literature. A NPL is a loan in which the borrower has defaulted in making repayment of the principal and interest for a period of time usually 90 or 180 days (Farn� e and Vouldis, 2024). NPLs are mostly associated with banks which are deposit taking and lending institutions. Banks issue loans to credit worthy borrowers and expect borrowers to make repayment of the loan principal and interest at a specific period (T€ ol€ o and Vir� en, 2021). Borrowers may default on loan repayment. When they do, the loan becomes non-performing. Bank NPL continues to attract the attention of bank managers, academics, economists, bank supervisors and financial regulators for seven reasons. One, a high level of NPL in the banking sector reduces the ability of banks to provide credit to the real economy to stimulate production and consumption towards economic growth. Two, NPL is procyclical with changing economic conditions. This means that NPL tends to be higher during economic Asian Journal of Economics and Banking 437 JEL Classification — G21, G28 © Peterson K. Ozili. Published in Asian Journal of Economics and Banking. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/ legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2615-9821.htm Received 5 September 2024 Revised 21 November 2024 26 April 2025 Accepted 28 May 2025 Asian Journal of Economics and Banking Vol. 9 No. 3, 2025 pp. 437-462 Emerald Publishing Limited e-ISSN: 2633-7991 p-ISSN: 2615-9821 DOI 10.1108/AJEB-09-2024-0103 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 downturns and lower during good economic times as was observed during the 2007–2009 global financial crisis and the COVID-19 pandemic (Ari et al., 2020; Alnabulsi et al., 2022). Three, NPL is important because minimizing the level of NPL is a central objective of banking supervision. Fewer NPL means greater bank stability and low risk of a financial/banking crisis. Four, NPL has a signaling power because it can signal to investors and depositors that a bank has poor management quality. It can also signal poor regulation and supervision of banks by financial regulators and bank supervisors. Five, banks with a large loan portfolio are exposed to loan default or credit risk when unfavorable economic conditions and unforeseen changes in borrowers’ characteristics make it difficult for borrowers to repay the loan owed to banks and other lending institutions (Serrano, 2021). Six, large NPL on bank income statement will decrease bank profit, erode bank capital, and make banks become financially unstable. Seven, bank NPL is also important to bank regulators and supervisors for financial stability reasons. Bank regulators usually determine the regulatory threshold for banking sector NPL (Kanoujiya et al., 2023). In many countries, bank regulators keep the NPL threshold at a single-digit percent level. After determining the threshold, bank regulators strive to use macroprudential and micro prudential regulatory tools to influence banks to meet the regulatory NPL target (Su� arez and S� anchez Serrano, 2018). Despite attempts by regulators and supervisors to control NPL, NPL is not easy to control and may become erratic due to loan recovery problems and the many unforeseen external factors that affect borrowers’ ability to repay loans as at when due (Bellotti et al., 2021; Foglia, 2022). These issues have made bank NPLs the focus of some heated debates in the banking regulation literature, and it reinforce the need to revisit the determinants of NPL and its consequences on financial institutions and the economy. Notably, there is growing demand by economists and policymakers for insightful research into the non-traditional determinants of NPL and the likely effect of NPL on banks and the economy in recent times. The demand for such research has never been higher than it is right now, especially now that policymakers are concerned about the potential effect of geopolitical tensions, rising inflation and tariff trade wars on bank NPLs. Valuable insight into the recent determinants of NPL and its effects can be gained from a comprehensive review of the recent NPL research in the literature. Presently, no study has presented an overview or a literature review of the post-2020 studies. Existing studies have not examined the recent non-traditional determinants of NPL or its effect on the economy. Having identified these gaps, this study presents a comprehensive review of the recent research into bank NPLs from different regions of the world to identify the nontraditional determinants of NPLs and the effect of NPL on banks and the economy. It also suggests some directions for further research. Before proceeding, I commend Manz (2019) and Chawla and Rani (2021)’s literature review that presented an overview of the determinants of NPL in the pre-2020 period. The present review complements the work of Manz (2019) and Chawla and Rani (2021) by identifying the recent determinants of NPL. However, the present review is significantly different from early literature review studies in several ways. One, the present review captures new developments in NPL research which are not documented in early literature review studies. Two, the present study provides a bibliometric analysis of the recent developments in the NPL literature. Such analysis was not provided in prior literature review studies. Three, the present review breaks down the recent NPL research according to regions, regional blocs, country-specific and cross-country studies in order to provide a good understanding of the regional developments in NPL research, and to make it easier to identify the gaps in the literature that needs to be filled by future research studies. Such break down was not provided in prior literature review studies. Therefore, there is a need to present a comprehensive overview of the recent research in the NPL literature and to identify the new developments (i.e. new determinants and effects) that need to be singled out and brought to the attention of scholars and policymakers to stimulate further research inquiry that would expand the scope of NPL research and offer some directions for future research. AJEB 9,3 438 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 The analysis in this review article contributes to the NPL literature in several ways. One, it contributes to existing literature review studies that present an overview of the determinants of bank NPL (e.g. Manz, 2019; Chawla and Rani, 2021), but which have not provided an overview of the non-traditional determinants of NPL in the recent literature since 2020. Two, the study contributes to the literature by using a bibliometric analysis to show academics and researchers the areas where more collaborative research is needed to advance the literature. Three, the study contributes to the NPL literature that examines the institutional factors affecting bank NPL (Hakimi et al., 2022; Giammanco et al., 2023; Ahiase et al., 2024). Four, it contributes to emerging studies that examine the adverse effects of NPL for bank performance, stability, and economic growth (Huljak et al., 2022; Duong et al., 2023; Sain and Kashiramka, 2023). Insights from this article can provide a better understanding of how NPL affects banks and the economy. Finally, the discussion in this review article can assist regulators and supervisors in identifying the macro “economic” and “non-economic” factors that exert some influence on bank NPL which they have not taken into consideration in their stress test activities. The rest of the study is organized as follows. Section 2 presents the methodology used to conduct the review. Section 3 presents the bibliometric analysis of the developments in NPL research. Section 4 presents the thematic review of the recent NPL literature. Section 5 presents the recent NPL research around the world. Section 6 presents the methodological advances and issues in NPL research. Section 7 suggests some areas for future research. Section 8 presents the conclusion of the study. 2. Methodology The study used the thematic and bibliometric literature review methodologies to conduct the review. A thematic review method is used to capture the major research areas, themes or issues in the NPL literature. A reproducible search strategy, inclusion criteria and screening method are used to obtain the articles included in the thematic literature review. The first criterion is the article search instrument. The study used Google Scholar as the main article search instrument because it is considered to be the world’s largest search engine that indexes the full text or metadata of scholarly research across many disciplines. Google Scholar is less restrictive compared to Scopus and Web of Science. More articles were obtained from Google Scholar compared to Scopus and Web of Science. Google Scholar is more inclusive because it allows the user to access and review articles which are not indexed in the Web of Science and Scopus. Google Scholar also makes it easier to find relevant articles that are not indexed by other search platforms. The second criterion is the sample period. The sample period for the review is from 2020 to mid-2024. This ensures that the study captures the relevant research the NPL literature in the last five years. The third criterion is language. Only the articles published in English language were used for the review. The fourth criterion is the article search and selection process. The articles were selected by inserting the keyword “non-performing loan” into Google Scholar search engine. This approach is important because it ensures that only the articles that focus on NPLs in the literature are selected. The resulting articles from Google Scholar search were used to conduct the literature review. 372 articles were found from Google Scholar search results. The 372 articles were used to conduct the bibliometric analysis in section 3. However, to conduct the thematic literature review in sections 4 and 5, the 372 articles were further screened for quality of the research, type of journal, article duplication, and non-relevance to NPL. This process reduced the articles to 96 articles which include peerreviewed journal articles and few working papers. 3. A bibliometric analysis of the developments in NPL research This section presents a bibliometric analysis of NPL research in the recent literature. The bibliometric analysis aims to provide a visualization of the developments in the recent NPL Asian Journal of Economics and Banking 439 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 literature and to understand the publication relationships. In this section, the stringent article selection criteria in section 2 are relaxed in order to obtain a large number of articles for the bibliometric analysis. 3.1 Total number of articles and the regional focus of regional NPL studies The total number of articles found in Google Scholar increased each year from 2020 to mid2024 (see Figure 1). It is also important to identify the geographical focus of recent NPL studies. Observation from Google Scholar search results – from the first page to the fifteenth page of the search results – show that regional NPL studies focus mostly on the European region, followed by the African region, and the Asian region (see Table 1). Regarding the NPL research focusing on the regional blocs, it can be observed that some studies examine NPL in MENA, BRICS, Association of Southeast Asian Nation and Gulf Cooperation Council (GCC) countries while only few NPL studies focus on the South Asian Association for Regional Cooperation (SAARC) and Organization for Economic Cooperation and Development (OECD) countries. This finding indicates that more NPL research is needed for SAARC and OECD countries and for the Asia–Pacific, North America, Latin America and Caribbean regions. 3.2 Countries with the most NPL research It is important to identify countries with the highest number of NPL research studies. Observation from Google Scholar search results – from the first page to the fifteenth page of the search results – show that there are more single-country NPL research studies focusing on China, Malaysia, Indonesia, and Kenya. In contrast, few NPL research studies focus on countries like India, Ethiopia, Zimbabwe, Brazil, United Arab Emirates (UAE), and Saudi Arabia. This finding indicates that more NPL studies are needed that focus on India, Ethiopia, Zimbabwe, Brazil, UAE, and Saudi Arabia (See Figure 2). 3.3 Top research areas/themes It is also important to identify the top research areas or research themes in the recent empirical literature (see Table 2). The topmost research areas are “the determinants of NPL”, “the impact of bank NPL on profitability”, “the behavior of NPL during the COVID-19 pandemic”, and “NPL as an indicator of bank risk and credit risk”. However, studies that conduct literature review on NPL are very scant in the recent literature. This indicates that more literature review studies are needed in the literature. 0 50 100 150 200 250 300 350 400 2020 2021 2022 2023 Mid-2024 251 244 312 367 159 Number Year Figure 1. Total number of research articles on NPL. Source: Google Scholar (2020 to mid-2024) AJEB 9,3 440 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 3.4 Common theories used in the recent NPL literature Several studies in the recent NPL literature use a number of theories or hypotheses to explain the behavior of NPL. The theories used in the recent NPL literature include agency theory, moral hazard theory, stakeholder theory, financial intermediation theory, information asymmetry theory, market power theory, capital buffer theory, liquidity preference theory, Table 1. Number of regional NPL studies Regions and regional blocs # Europe 34 Africa 24 Asia 16 Asia–Pacific 4 North America 2 Latin America and Caribbean 1 Brazil, Russia, India, China, and South Africa (BRICS) countries 11 Middle East and North Africa (MENA) countries 10 Association of Southeast Asian Nations (ASEANs) countries 10 Gulf Cooperation Council (GCC) countries 6 South Asian Association for Regional Cooperation (SAARC) countries 5 Organization for Economic Cooperation and Development (OECD) countries 2 Note(s): Output is generated from the manual count of the search result from Google Scholar from 2020 to mid-2024 Source(s): Google Scholar (from 2020 to mid-2024) 02468 10 12 Kenya Indonesia Malaysia China Nigeria Tanzania Vietnam Italy Turkey India South Africa Bangladesh Ghana Bosnia and Herzegovina Namibia Ethiopia Zimbabwe Brazil UnitedArab Emirates Saudi Arabia Number of ar�cles Countries Figure 2. Number of single-country NPL research articles. Source: Google Scholar (2020 to mid-2024) Asian Journal of Economics and Banking 441 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 loan pricing theory, and trade-off theory (see Table 3). The most common theories used by NPL scholars are the agency theory, stakeholder theory, the information asymmetry theory and the moral hazard theory. Among these three theories, the moral hazard theory and agency theory have proven to be the most effective in explaining the behavior of NPL because the two theories argue that the lack of monitoring of borrowers is a potential cause of rising nonperforming loans. If borrowers are constantly monitored, lenders will be able to prevent borrowers from engaging in activities, or taking actions, that impair their ability to repay loans. Also, the theories that have been newly applied to NPL research include the market power theory, capital buffer theory, liquidity preference theory, loan pricing theory, and trade-off theory. These theories have been newly applied to NPL research because of the growing interdisciplinary nature of NPL research and the need to use theories from the economics discipline to explain the behavior of NPL. Table 2. Top research areas/themes in the recent NPL Literature Rank Research areas/Themes Number of articles 1st Determinants of NPL/Factors affecting NPL 112 2nd Impact of NPL on bank profitability 56 3rd Behavior of NPL during the COVID-19 pandemic 45 4th NPL as an indicator of bank risk and credit risk 35 5th NPL and financial stability 32 6th NPL and bank lending 21 7th NPL and financial/banking crisis 19 8th Literature review on NPL 9 Source(s): Google Scholar (curated on 17/06/2024); Google Scholar (from 2020 to mid-2024) Table 3. Common theories used in the recent NPL literature Rank Theory Studies that have used the theory to explain NPL 1 Agency theory Ngungu and Abdul (2020), Owonye and Obonofiemro (2022), Kim Quoc Trung (2022), Tarchouna et al. (2022), Wengerek et al. (2022) 2 Moral hazard hypothesis/ theory Mohamad and Jenkins (2021), Lee et al. (2020), Cicchiello et al. (2022) 3 Stakeholder theory Kim Quoc Trung (2022), Liu et al. (2023), Iqbal and Nosheen (2023) 4 Information asymmetry theory Owonye and Obonofiemro (2022), Do et al. (2020), Olarewaju (2020), Park and Shin (2021) 5 Financial intermediation theory Owonye and Obonofiemro (2022), Alnabulsi et al. (2023a) 6 Market power theory Ngungu and Abdul (2020) 7 Capital buffer theory Ngungu and Abdul (2020) 8 Liquidity preference theory Ngungu and Abdul (2020) 9 Loan pricing theory Owonye and Obonofiemro (2022) 10 Trade-off theory Duong et al. (2023) 11 Diversification theory Duong et al. (2023) 12 Pecking Order theory Duong et al. (2023) 13 Modern portfolio theory Do et al. (2020) 14 Charter value theory Cicchiello et al. (2022) Source(s): Google Scholar (from 2020 to mid-2024) AJEB 9,3 442 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 3.5 Journals with the highest single-study citation It is also important to recognize the journals in which the most-cited articles have been published in, as these journals tend to have a wider readership. Table 4 shows that three journals have over 100 citations since 2020 namely the “Asian Journal of Accounting Research”, “Journal of Banking and Finance”, and “Finance Research Letters”. These journals are high-impact journals that publish scholarly research on NPL. The “Asian Journal of Accounting Research” ranks first among the journals listed in Table 4. 4. Thematic review of the recent NPL literature This section presents the dominant themes in the recent NPL literature which are the bankspecific determinants of NPL, external determinants of NPL, effect of NPL on bank lending, effect of NPL on bank performance, effect of NPL on financial stability and the effect of NPL on the wider economy. The summary is presented in Table 5. 4.1 Bank-specific determinants of NPL The recent empirical literature identifies several bank-specific determinants of NPL which can be divided into bank-specific “financial” determinants of NPL and the bank-specific “nonfinancial” determinants of NPL. The identified bank-specific “financial” determinants of NPL include loan growth, net interest margin, loan loss provision, bank diversification, operating efficiency, bank size, bank profit, interest rate (Ahmed et al., 2021); income diversification (Ciukaj and Kil, 2020; Khan et al., 2020; Risti� c and Jemovi� c, 2021); capital adequacy ratio (Kryzanowski et al., 2023; Pancotto et al., 2024); operating cost (Nguyen, 2024); return on equity (Erdas and Ezanoglu, 2022); return on asset (Kjosevski and Petkovski, 2021); liquidity ratio (Msomi, 2022), and bank business model (Farn� e and Vouldis, 2024). The identified bankspecific “non-financial” determinant of NPL in the recent empirical literature is corporate governance (Tarchouna et al., 2022). Table 4. Journals with the highest single-study citation Journals Top citations (as at 17th June 2024) Scopus quartile ranking (as of June 2024) The article Asian Journal of Accounting Research 234 Q2 Khan et al. (2020) Finance Research Letters 132 Q1 Karadima and Louri (2021) Journal of Banking and Finance 109 Q1 Ari et al. (2021) Journal of Asian Finance, Economics and Business 97 Nil Singh et al. (2021) International Journal of Forecasting 91 Q1 Bellotti et al. (2021) Journal of Risk and Financial Management 76 Q1 Ahmed et al. (2021) North American Journal of Economics and Finance 73 Q1 Serrano (2021) Journal of Economic Studies 64 Q1 Staehr and Uusk€ ula (2021) International Review of Financial Analysis 53 Q1 Karadima and Louri (2020) Journal of Central Banking Theory and Practice 51 Q2 � Zuni� c et al. (2021) Note(s): The citation counts are obtained from Google Scholar while the quartile ranking are obtained from Scopus database as at June 2024 Source(s): Google Scholar (2020 to mid-2024) Asian Journal of Economics and Banking 443 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 4.2 External determinants of NPL The recent empirical literature also identifies several external determinants of NPL which can be divided into macro “economic” determinants of NPL and the macro “non-economic” determinants of NPL. The identified macro “economic” determinants of NPL include exchange rate (Nathan et al., 2020; Ahmed et al., 2021); economic growth (Nguyen, 2024); gross domestic product (GDP) (Erdas and Ezanoglu, 2022); the lending rate or interest rate in the economy (Nathan et al., 2020); inflation rate (Msomi, 2022); unemployment rate (Risti� c and Jemovi� c, 2021); the economic environment and loose financing conditions (GambaSantamaria et al., 2024); public debt (Foglia, 2022); economic policy uncertainty (EPU) (Karadima and Louri, 2021; Ozili, 2022b); current account balance, real house prices (Staehr and Uusk€ ula, 2021); sovereign debt and money supply (Anita et al., 2022). The identified macro “non-economic” determinants of NPL include political risk (Ahmed et al., 2021); fintech inputs (Wang et al., 2023); the systemic risk status of the bank (Ozili, 2020); the Table 5. Summary of the thematic review of the recent NPL literature S/ N Major themes Findings in the theme Studies 1 Bank-specific determinants of NPL Loan growth; net interest margin; loan loss provision; bank diversification; operating efficiency; bank size; bank profit; interest rate; capital adequacy ratio; return on equity; return on asset; liquidity ratio; bank business model; corporate governance Ahmed et al. (2021), Ciukaj and Kil (2020), Khan et al. (2020), Risti� c and Jemovi� c (2021), Kryzanowski et al. (2023), Pancotto et al. (2024), Nguyen (2024), Erdas and Ezanoglu (2022), Tarchouna et al. (2022), Farn� e and Vouldis (2024), Msomi (2022), Kjosevski and Petkovski (2021) 2 External determinants of NPL Exchange rate; economic growth; GDP; the lending rate; inflation rate; unemployment rate; the economic environment; loose financial conditions; public debt; economic policy uncertainty; current account balance; real house prices; sovereign debt; money supply; political risk; fintech inputs; the systemic risk status of the bank; the COVID-19 pandemic; the independence and transparency of the central bank; the fintech era; the size of shadow banking activity; financial inclusion; country risks; governmental ineffectiveness; and regulatory quality Nathan et al. (2020), Nguyen (2024), Erdas and Ezanoglu (2022), Msomi (2022), Risti� c and Jemovi� c (2021), Gamba-Santamaria et al. (2024), Foglia (2022), Karadima and Louri (2021), Staehr and Uusk€ ula (2021) Anita et al. (2022), Kryzanowski et al. (2023), Wang et al. (2023), Mamoon et al. (2025), Isayev and Farooq (2024), Ozili and Adamu (2021), Ahiase et al. (2024), Ozili (2020) 3 Effect of NPL on bank lending High NPL decreases bank lending. It decreases lending to the real economy after the global financial crisis T€ ol€ o and Vir� en (2021), Gjeçi et al. (2023), Serrano (2021), Ahmed et al. (2024) 4 Effect of NPL on bank performance High NPL decreases the return on equity, the profitability or financial performance of banks. It also decreases bank efficiency Lawrence et al. (2024), Kumari et al. (2024), Iqbal and Saeed (2023), Duong et al. (2023), Phung et al. (2022) 5 Effect of NPL on financial stability High NPL increases financial instability, financial distress and it leads to higher loan loss provisions Kulu and Osei (2024), Sain and Kashiramka (2023), Alnabulsi et al. (2023b), Elfergani et al. (2024) 6 Effect of NPL on the wider economy High NPL leads to a decrease in foreign direct investment inflow, decline in real GDP growth, low levels of financial inclusion Alam et al. (2024), Huljak et al. (2022), Zhang et al. (2022) Source(s): Summary of author’s regional review of literature AJEB 9,3 444 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 examines the correlation of EPU and bank NPL from 2008 to 2017 and finds that EPU is negatively correlated with bank NPL in G7 countries. Saliba et al. (2023) focus on the BRICS countries. They investigate the impact of country risks on banking sector NPL in Brazil, Russia, India, China, and South Africa (BRICS) countries. They assess whether countryspecific risks affect bank NPL during the 2004 to 2020 period and using the quantile estimation method. They find that higher country risks increase bank NPL in BRICS countries. Kumar et al. (2023) focus on the GCC countries. They investigate the determinants of bank NPL in GCC countries from 2000 to 2018. They analyze the data of 53 conventional banks using the system GMM regression method and find that high non-oil real GDP growth rate and high inflation increase NPL while elevated levels of domestic private credit and a high volatility index lower the size of NPL. In summary, evidence from other regions shows that the determinants of bank NPL are mostly bank-specific and macro factors such as return on equity, credit growth, credit costs, EPU, country risks, non-oil real GDP growth rate, inflation, domestic private credit and the volatility index. Meanwhile, the determinants which are common among the empirical studies reviewed in this section are GDP and GDP growth. 5.6 Single country studies Single country studies are important because the findings from such studies often take into account the peculiar characteristics of each country and the unique economic factors that are prevailing in the country which may affect bank NPL. Many single country studies have emerged since 2020. For instance, in the case of the United States, Tarchouna et al. (2022) examine the impact of banks’ corporate governance on NPL. They examine 184 US commercial banks from 2000 to 2013 and using the dynamic panel GMM estimation. They find that small banks with weak corporate governance systems have high NPL. In the case of China, Wang et al. (2023) examine whether fintech inputs are a determinant of bank NPL. They analyze 432 branches of the commercial banks in the city of Beijing in China from 2005 to 2022. The fintech inputs used in their study are the personnel inputs, software inputs, and hardware inputs which are used in banking operations. They find that fintech inputs decrease the level of NPL, but the fintech inputs have a lag effect in reducing the size of NPL. In a related study, Kryzanowski et al. (2023) examine the effect of the COVID-19 pandemic on the NPL of Chinese banks. They find that the COVID-19 pandemic increases bank NPL while wellcapitalized banks are more effective in controlling their NPL ratio during the pandemic. Furthermore, in Vietnam, Nguyen (2024) analyzes the determinants of NPL in Vietnamese banks from 2005 to 2020. They show that bank profits, bank size and economic growth decrease NPL while operating cost and loan loss provisions increase NPL. In another related study, Trung (2024) investigate the impact of the COVID-19 pandemic on bank NPL in Vietnam from 2011 to 2021. They find that the COVID-19 pandemic increases the NPL of Vietnamese banks. In the case of Pakistan, Khan et al. (2020) focus on the determinants of bank NPL in Pakistan from 2005 to 2017. They analyze listed commercial banks using fixed and random effects panel regression methods. They find that operating efficiency and profitability have a significant negative effect on NPL, while capital adequacy and income diversification have a negative effect on NPL. In a related study, Ahmed et al. (2021) examine the macroeconomic and bank-specific determinants of NPL in Pakistan from 2008 to 2018 using the system GMM estimator. They find that credit growth, net interest margin, loan loss provisions, operating efficiency, bank size, and GDP growth are determinants of NPL. In the case of Italy, Foglia (2022) investigate the macroeconomic determinants of NPL in Italy from 2008Q3 to 2020Q4 using the autoregressive distributed lag (ARDL) cointegration model. The author finds that GDP, public debt, unemployment rate and domestic credit are determinants of the NPL of Italian banks. In a related study, Pancotto et al. (2024) investigate the determinants of NPL in the Italian banking sector from 2011 to 2017 and using dynamic panel data methods. They find that well-capitalized banks have fewer NPL. Asian Journal of Economics and Banking 451 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 In the context of Uganda, Nathan et al. (2020) examine the determinants of NPL in Uganda’s commercial banks using quarterly data for the period 2002Q1 to 2017Q2 and using ARDL and bounds test techniques. They find that a higher lending rate, real effective exchange rate and unemployment rate lead to higher NPL while higher return on asset and GDP growth rate decrease NPL. In the case of Serbia, Risti� c and Jemovi� c (2021) analyze the NPL determinants in Serbia from 2010–2019 and using a vector autoregressive model. They show that GDP, inflation, unemployment, return on assets, cost efficiency, capital adequacy ratio and income diversification are significant determinants of NPL. In the case of Colombia, GambaSantamaria et al. (2024) examine the determinants of NPL in Colombia. They find that a favorable economic environment and loose financial conditions decrease the size of NPL in Colombian banks. � Zuni� c et al. (2021) examine the case of Bosnia and Herzegovina. � Zuni� c et al. (2021) examine the determinants of bank NPL and the effect of the COVID-19 pandemic on bank NPL in Bosnia and Herzegovina, after controlling for GDP and loan loss provisions from 2012 to 2020. The authors use the linear time series multiple regression method and show that GDP growth has a positive impact on NPL while the COVID-19 pandemic has a negative impact on NPL. Finally, despite the peculiarities of each country reviewed in this section, it can be seen that some countries report similar determinants of NPL despite each country being different. The common determinants of NPL are GDP growth, COVID-19 pandemic, unemployment, inflation, GDP, operating efficiency, interest rate, bank size, bank profit and capital adequacy ratio. 5.7 Cross-country or international NPL research Cross-country studies are also important because the findings from such studies can reveal the common factors across countries that affect NPL. Many cross-country studies have emerged since 2020. For instance, Mamoon et al. (2025) investigate whether central bank independence and transparency are determinants of bank NPL using data from 39 countries. They find that independent central banks experience fewer NPL in the banking industry. They also find that transparent central banks with a lower degree of information symmetry experience fewer NPL. Ozili (2022a) investigate the behavior of bank NPL in the fintech era. The study analyses 35 developed countries from 1998 to 2016 and show that NPL is fewer in the fintech era. It was also found that countries with high domestic private credit have higher NPL in the fintech era. Karadima and Louri (2021) argue that EPU might initiate and propagate NPL. To validate their argument, they examine whether EPU has a significant effect on NPL. They examine 507 banks from four countries: France, Germany, Italy, and Spain from 2005 to 2017. They find that EPU has a positive impact on NPL, but the positive impact is moderated by higher bank concentration. Ozili and Adamu (2021) examine whether financial inclusion is a determinant of bank NPL using data from 48 countries. They use the fixed effect panel regression method and find that greater financial inclusion, in terms of formal account ownership, leads to higher NPL. Isayev and Farooq (2024) examine the impact of shadow banking activity on NPL in listed banks in 27 countries from 2002 to 2020. They find that banks headquartered in countries with high shadow banking activity have fewer NPL. Kjosevski and Petkovski (2021) examine the macroeconomic and bank-specific determinants of NPL focusing on 21 commercial banks from three Baltics States namely Estonia, Latvia and Lithuania from 2005 to 2016. They use the GMM regression method and find that macroeconomic factors such as GDP growth, public debt, inflation, and unemployment have a significant effect on NPL while bank-specific factors such as equity to total assets ratio, return on assets, return on equity, and loan growth have a significant effect on NPL. Ozili (2020) analyses how the state of the economy affects the NPL of European global systemically important banks compared to the European global non-systemically important banks from 2004 to 2013. The author uses the panel regression method to analyze the link between NPL and GDP growth and find that global systemically important banks have fewer NPL during economic booms and during periods of increased lending, while global nonAJEB 9,3 452 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 systemically important banks experience higher NPL during periods of increased lending. Kartika et al. (2022) examine the effect of corporate governance on bank NPL. They analyze 440 banks in emerging market countries using Bloomberg data from 2016 to 2020. They use partial least squares regression method to analyze the data and find that financial performance is a more significant determinant of NPL than bank corporate governance indicators. Adusei and Adeleye (2022) examine the effect of credit information sharing and creditor rights protection on bank NPL in 132 countries. They find that, in the presence of creditor rights protection, the positive impact of credit information sharing on NPL is higher. Also, creditor rights protection reduces NPL in the presence of credit information sharing. Finally, the common determinant of NPL among the cross-country studies reviewed in this section is EPU. 6. Methodological advances and issues in NPL research Quantitative research methods are mostly used in the recent empirical literature while qualitative methods are used sparingly. 6.1 Common baseline model used in the recent literature The common baseline model adopted in most quantitative NPL studies is the multi-factor linear model which expresses NPL as a function of its determinants, where the NPL ratio is the dependent variable while its determinants are the independent variables (see Karadima and Louri, 2021; Phung et al., 2022; Anita et al., 2022). Depending on the objective of the researcher, the NPL model may be expressed as a function of the bank-specific determinants of NPL, or the external determinants of NPL or a combination of both the bank-specific and external determinants of NPL. The common internal and external determinants of NPL used in the recent literature are highlighted in sections 4.1 and 4.2. 6.2 Common estimation method used in the recent literature The common estimation method used in the recent empirical literature is the panel regression estimation method while the use of time series regression is less common (see T€ ol€ o and Vir� en, 2021; Karadima and Louri, 2021; � Zuni� c et al., 2021; Phung et al., 2022; Anita et al., 2022). When using the panel regression method, researchers often make econometric adjustments to the variables in the model by adding fixed effects, random effects, robust standard errors, firstdifference, lagged NPL variable and other econometric adjustments that achieve the modelling objective of the researcher (see, for example, Khan et al., 2020; Hernawati et al., 2021; Ozili and Adamu, 2021; T€ ol€ o and Vir� en, 2021; Karadima and Louri, 2021; Phung et al., 2022; Anita et al., 2022). A major advancement in the recent empirical NPL literature is the frequent use of robust methodologies. These methodologies include quantile regression, two-way system GMM regression method, and the ARDL cointegration model, among others (see Table 7). These methodologies are preferred in NPL research because they are effective in addressing simultaneity bias, endogeneity problems, omission bias and collinearity problems in the dataset. Another notable advancement in the literature is the continuous discovery of new nontraditional determinants of NPL such as digital financial inclusion, corruption, shadow banking, volatility index, fintech inputs and the level of sustainable development, among others (see, for example, Mohamad and Jenkins, 2021; Hakimi et al., 2022; Wang et al., 2023; Ozili, 2024; Kumar et al., 2023; Isayev and Farooq, 2024). These discoveries are helping to increase knowledge of the diverse factors that affect NPL, and they are also helping to reduce the size of the error term of NPL models by incorporating new predictors into NPL models. 6.3 Common sample period analysis and source of NPL data In terms of the sample period covered in the recent empirical literature, many studies examine the post-financial crisis period (e.g. Ciukaj and Kil, 2020; Serrano, 2021; T€ ol€ o and Vir� en, 2021; Mohamad and Jenkins, 2021; Alaoui Mdaghri, 2022; Asemota et al., 2023; Chinoda and Asian Journal of Economics and Banking 453 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 Kapingura, 2023; Ali et al., 2023; Zeqiraj et al., 2024). Other studies compare the post-crisis period with the pre-crisis period (e.g. Nor et al., 2021; Hakimi et al., 2022; Alnabulsi et al., 2022; Huljak et al., 2022). Few studies compare the global financial crisis period with the COVID-19 pandemic period (Alnabulsi et al., 2022), while other studies focus on the COVID19 pandemic period alone (Park and Shin, 2021; Apergis, 2022). Regarding the source of NPL data, there are numerous database that offer open-access and closed-access information on bank-level NPL and industry (or country-level) NPL. They include BankFocus database (formerly, Bankscope database), Compustat, Statista, the World Bank’s Global Financial Development Indicators, central bank website, banks’ financial statements, Datastream and Thomson One Banker. 6.4 Variable measurement issues Turning to the methodological issues in the recent empirical literature, a major methodological issue is the observed variation in the measurement of NPL – the dependent variable. Some studies use the actual NPL amount (without a deflator) as the dependent variable ( � Zuni� c et al., 2021). Other studies use the NPLs to total loans ratio (see, for example, Kjosevski and Petkovski, 2021; Chun and Ardaaragchaa, 2024). Another issue is the lack of universal consensus on what the deflator of the NPL ratio should be. Some studies use total loans as the deflator which yields the NPLs to total loans ratio (e.g. Kjosevski and Petkovski, 2021; Chun and Ardaaragchaa, 2024). Other studies use lagged total loans as the deflator which yields the NPLs to lagged total loans ratio (Chen et al., 2021). Some studies use total assets as the deflator which yields the NPLs to total assets ratio (L� opez-Espinosa et al., 2021). These variations in the NPL ratio deflator make comparison of the results of NPL research difficult. 7. Areas for future research More NPL research is needed from Pacific countries, Latin America and Caribbean countries. Presently, only a few studies examine the determinants of NPL in Pacific countries, Latin Table 7. Common methodologies used in the recent empirical NPL literature Common methodologies Studies 1 Panel least squares regression Serrano (2021), Ciukaj and Kil (2020), El-Chaarani et al. (2023a, b) 2 Quantile regression Serrano (2021), Saliba et al. (2023) 3 Time series linear multiple regression � Zuni� c et al. (2021) 4 Panel OLS regression with fixed and random effects T€ ol€ o and Vir� en (2021), Khan et al. (2020), Karadima and Louri (2021), Phung et al. (2022), Anita et al. (2022), Ozili and Adamu (2021), Hernawati et al. (2021) 5 System and dynamic GMM regression method Ahmed et al. (2021), Hakimi et al. (2024), Nor et al. (2021), Adesina and Mwamba (2021), Olarewaju (2020) 6 Autoregressive distributed lag (ARDL) cointegration model Foglia (2022), Kalu et al. (2021) 7 Dynamic panel regression Wang et al. (2023), Vithessonthi (2023) 8 panel Bayesian VAR model Huljak et al. (2022) 9 Structural equation modeling based on partial least squares Kartika et al. (2022) 10 Pearson correlation Ozili (2024) 11 Qualitative methods Trung (2024) 12 Seemingly unrelated regressions estimator Ali et al. (2023) 13 Two-stage least squares regression Adu (2022) Source(s): Google Scholar (2020-mid-2024) AJEB 9,3 454 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 America, and Caribbean countries. These countries have the lowest number of published empirical NPL research. Determining the factors that affect NPL in these countries is important because it can help to compare the findings of such studies with the findings from African countries to determine the similarities or differences in NPL determinants, and which could lead to further comparative research in the NPL literature. Therefore, future research studies should investigate the determinants of NPL in Pacific countries, Latin America, and Caribbean countries. Two, qualitative studies on NPL are scarce in the literature. There is a need for more qualitative studies on NPL. Researchers should use interviews, questionnaires, or surveys to elicit the opinions of bank managers and credit risk offers on the causes and consequences of NPL. The response data generated from the interviews, questionnaires or surveys of bank managers and credit risk officers can offer new insights that cannot be gained from using secondary data or quantitative research methods. Therefore, future research studies should undertake more qualitative research when investigating the determinants and consequences of NPL. Three, more research is needed on the signaling effect of NPL. Rising NPL may signal poor management of bank loan portfolio to bank owners. It may also signal the effect of an unfavorable economic condition on banks’ lending activities. It may also signal other things such as a weak loan recovery system. NPL may have other signaling effects which remain unknown and unexplored in the literature. Therefore, future research should provide new insights that improve our understanding of the numerous signaling effects of NPL. Four, additional research is needed on the effect of cultural factors and religiosity on NPL. Cultural factors and religiosity may influence borrowers’ willingness to repay the loans owed to financial institutions. The recent NPL literature is silent about the role of cultural factors and religiosity in influencing the size of bank NPL. Future research studies should examine whether cultural factors and religiosity are potential determinants of NPL. Five, there is a need to examine the effect of loan repayment digital technologies on NPL. There are existing digital technologies, such as mobile banking applications, that assist retail and corporate borrowers in repaying their loans remotely and quickly, thereby reducing the risk of loan default and decreasing the level of NPLs. The existing literature has not considered the role of such digital technologies in reducing the NPL. Therefore, future studies should consider loan repayment digital technologies as potential determinants of NPL. Six, there is a need to consider the impact of the regulatory/supervisory style of the bank regulator/supervisor on bank NPL. This is important because commercial banks will do all it takes to minimize NPL if they understand that the bank regulator/supervisor is adopting a regulatory/supervisory style that signals to the banking industry that the regulator/supervisor is willing to allow a bank to fail if the bank is in severe distress and without a guarantee of central bank bailout. Such regulatory/supervisory inclination or style will incentivize banks to increase their effort to mitigate NPL. Future studies should test this hypothesis using empirical data. Seven, there is a need to continuously evaluate the impact of regulation on bank NPL. For example, the Basel Committee on Banking Supervision implemented a regulation in 2023 that require NPL securitization exposures to be subject to 100% risk weight or higher, except for positions risk-weighted using external ratings-based approach. This regulation can increase the incidence of NPL in banks. Therefore, future studies should investigate the impact of such regulatory changes on bank NPL to offer insight into how new regulation may affect bank NPL and its implication for financial stability. 8. Conclusion This literature review article examined the recent research into bank NPLs. It identified the major themes in the literature including the recent determinants of NPL and its effects across different regions of the world while also proffering some suggestions for future research. Asian Journal of Economics and Banking 455 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 The findings of the review revealed that recent NPL studies have made significant progress in examining the interaction between NPL and a wide range of factors such as bank-specific factors, macroeconomic factors, country-level factors, and some consequences of NPL. The newly identified determinants of NPL in the recent literature are corporate governance, fintech, financial inclusion, country risks, regulatory quality, political risks, shadow banking activity, the COVID-19 pandemic, public/external debt, real house prices, and the independence of the central bank. The common regional determinants of NPL in the recent literature are corruption, GDP, debt, loan growth, inflation, capital adequacy ratio, lending rate, competition, the regulatory environment, and GDP growth (see Figure 3). The findings also showed that more NPL research is needed from SAARC and OECD countries, and from the Asia–Pacific, North America, Latin America and Caribbean regions. It was also shown that fewer NPL research has been conducted on India, Ethiopia, Zimbabwe, Brazil, the UAE, and Saudi Arabia. The common theories used by NPL scholars are the agency theory, stakeholder theory, the information asymmetry theory, and the moral hazard theory while the common methodologies used are the panel regression and system GMM regression methods. The findings of this review have several implications. One, the empirical evidence presented in this study can point policymakers to the areas where they need to pay more attention to, to control the level of NPL in the banking industry and preserve bank stability. Two, it provides scholars with new empirical insights into what is known and what is not known so that scholars can focus their research efforts on the most important themes in NPL research. Three, while existing theories may not explain the new determinants of NPL, there may be a need for new theories that explain the new emerging determinants of NPL particularly the linkages between NPL and technological factors such as fintech. Four, the evidence presented on the consequences of NPL can assist bank managers and policymakers in understanding the consequences of NPL for financial/banking stability and in understanding the type of safeguards to put in place to minimize the adverse effects of non-performing loans. A limitation of the study is that the study did not review the studies that were published prior to 2020. The review only focused on studies published from 2020 to 2024. Another limitation of the study is that it relied on a single search platform which is Google Scholar. The study did not use alternative search platforms which may identify additional articles that may offer new insights to enrich the review. Several directions for future research were suggested such as the need to conduct more qualitative NPL research, the need to investigate the signaling effects of NPL, the need to Figure 3. Summary of review results. Source: Author AJEB 9,3 456 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 explore the effect of cultural factors and religiosity on NPL, the role of loan repayment digital technologies in reducing the incidence of NPL, and the impact of regulation on bank NPL. References Adesina, K.S. and Mwamba, J.W.M. (2021), “Bank risk-taking behavior in Africa: the influence of net stable funding ratio”, The Journal of Developing Areas, Vol. 55 No. 2, doi: 10.1353/ jda.2021.0042. Adu, D.A. (2022), “Competition and bank risk-taking in Sub-Saharan Africa countries”, SN Business and Economics, Vol. 2 No. 7, p. 80, doi: 10.1007/s43546-022-00250-1. Adusei, M. and Adeleye, N. (2022), “Credit information sharing and non-performing loans: the moderating role of creditor rights protection”, International Journal of Finance and Economics, Vol. 27 No. 4, pp. 4756-4769, doi: 10.1002/ijfe.2398. Ahiase, G., Andriana, D., Agbemava, E. and Adonai, B. (2024), “Macroeconomic cyclical indicators and bank non-performing loans: does country governance matter in African countries?”, International Journal of Social Economics, Vol. 51 No. 1, pp. 62-80, doi: 10.1108/ijse-112022-0729. Ahmed, S., Majeed, M.E., Thalassinos, E. and Thalassinos, Y. (2021), “The impact of bank specific and macro-economic factors on non-performing loans in the banking sector: evidence from an emerging economy”, Journal of Risk and Financial Management, Vol. 14 No. 5, p. 217, doi: 10.3390/jrfm14050217. Ahmed, S.A., Sayed, O.A. and Ahmed, I.E. (2024), “The bank lending behaviour: does non-performing loans matter? Evidence from the top-ten banks in the Arab world”, Asian Development Policy Review, Vol. 12 No. 2, pp. 100-110, doi: 10.55493/5008.v12i2.4997. Alam, M.B., Tahir, M. and Omar Ali, N. (2024), “Do credit risks deter FDI? Empirical evidence from the SAARC countries”, Journal of Economics, Finance and Administrative Science, Vol. 29 No. 57, pp. 42-56, doi: 10.1108/jefas-09-2021-0191. Alaoui Mdaghri, A. (2022), “How does bank liquidity creation affect non-performing loans in the MENA region?”, International Journal of Emerging Markets, Vol. 17 No. 7, pp. 1635-1658, doi: 10.1108/ijoem-05-2021-0670. Ali, H., Gueyie, J.P. and Chrysostome, E.V. (2023), “Gender, credit risk and performance in subSaharan African microfinance institutions”, Journal of African Business, Vol. 24 No. 2, pp. 235-259, doi: 10.1080/15228916.2022.2079275. Alnabulsi, K., Kozarevi� c, E. and Hakimi, A. (2022), “Assessing the determinants of non-performing loans under financial crisis and health crisis: evidence from the MENA banks”, Cogent Economics and Finance, Vol. 10 No. 1, 2124665, doi: 10.1080/23322039.2022.2124665. Alnabulsi, K., Kozarevi� c, E. and Hakimi, A. (2023a), “Non-performing loans and net interest margin in the MENA region: linear and non-linear analyses”, International Journal of Financial Studies, Vol. 11 No. 2, p. 64, doi: 10.3390/ijfs11020064. Alnabulsi, K., Kozarevi� c, E. and Hakimi, A. (2023b), “Non-performing loans as a driver of banking distress: a systematic literature review”, Commodities, Vol. 2 No. 2, pp. 111-130, doi: 10.3390/ commodities2020007. Anita, S.S., Tasnova, N. and Nawar, N. (2022), “Are non-performing loans sensitive to macroeconomic determinants? An empirical evidence from banking sector of SAARC countries”, Future Business Journal, Vol. 8 No. 1, p. 7, doi: 10.1186/s43093-022-00117-9. Apergis, N. (2022), “Convergence in non-performing loans across EU banks: the role of COVID-19”, Cogent Economics and Finance, Vol. 10 No. 1, 2024952, doi: 10.1080/23322039.2021.2024952. Arham, N., Salisi, M.S., Mohammed, R.U. and Tuyon, J. (2020), “Impact of macroeconomic cyclical indicators and country governance on bank non-performing loans in Emerging Asia”, Eurasian Economic Review, Vol. 10 No. 4, pp. 707-726, doi: 10.1007/s40822-020-00156-z. Ari, A., Chen, S. and Ratnovski, L. (2020), COVID-19 and non-performing loans: lessons from past crises. SSRN 3632272. Asian Journal of Economics and Banking 457 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 Ari, A., Chen, S. and Ratnovski, L. (2021), “The dynamics of non-performing loans during banking crises: a new database with post-COVID-19 implications”, Journal of Banking and Finance, Vol. 133, 106140, doi: 10.1016/j.jbankfin.2021.106140. Asemota, G., Erhi, M. and Ihensekhien, O. (2023), “Do commercial banks’ non-performing loans respond to environmental financial and economic influences in the sub-saharan region of Africa”, Acta Universitatis Danubius. Oeconomica, Vol. 19 No. 2, pp. 269-288. Bellotti, A., Brigo, D., Gambetti, P. and Vrins, F. (2021), “Forecasting recovery rates on nonperforming loans with machine learning”, International Journal of Forecasting, Vol. 37 No. 1, pp. 428-444, doi: 10.1016/j.ijforecast.2020.06.009. Boussaada, R., Hakimi, A. and Karmani, M. (2022), “Is there a threshold effect in the liquidity risk–non-performing loans relationship? A PSTR approach for MENA banks”, International Journal of Finance and Economics, Vol. 27 No. 2, pp. 1886-1898, doi: 10.1002/ijfe.2248. Brei, M., Jacolin, L. and Noah, A. (2020), “Credit risk and bank competition in Sub-Saharan Africa”, Emerging Markets Review, Vol. 44, 100716, doi: 10.1016/j.ememar.2020.100716. Chawla, S. and Rani, S. (2021), “What determines non-performing loans? A systematic literature survey and directions for prospective research”, Global Business and Economics Review, Vol. 25 No. 2, pp. 154-176, doi: 10.1504/gber.2021.118213. Chen, L., Emanuel, D., Li, L.Z. and Yang, M. (2021), “Regulatory changes and loan loss provisions management by Chinese banks”, Journal of Accounting in Emerging Economies, Vol. 11 No. 4, pp. 651-675, doi: 10.1108/jaee-05-2020-0116. Chinoda, T. and Kapingura, F.M. (2023), “The impact of digital financial inclusion and bank competition on bank stability in sub-Saharan Africa”, Economies (Basel), Vol. 11 No. 1, p. 15, doi: 10.3390/economies11010015. Chun, S.H. and Ardaaragchaa, N. (2024), “Analysis of factors affecting the loan growth of banks with a focus on non-performing loans”, Journal of Risk and Financial Management, Vol. 17 No. 5, p. 203, doi: 10.3390/jrfm17050203. Cicchiello, A.F., Cotugno, M., Perdichizzi, S. and Torluccio, G. (2022), “Do capital buffers matter? Evidence from the stocks and flows of nonperforming loans”, International Review of Financial Analysis, Vol. 84, 102369, doi: 10.1016/j.irfa.2022.102369. Ciukaj, R. and Kil, K. (2020), “Determinants of the non-performing loan ratio in the European Union banking sectors with a high level of impaired loans”, Economic and Business Review, Vol. 6 No. 1, pp. 22-45, doi: 10.18559/ebr.2020.1.2. DaSouza, D., Martin, K., Abraham Jr, P. and Davis, G. (2023), “COVID-19 and financial institution stability: stress testing the Eastern Caribbean currency union”, Journal of Financial Regulation and Compliance, Vol. 31 No. 5, pp. 525-545, doi: 10.1108/jfrc-10-2022-0123. Do, H., Ngo, T. and Phung, Q. (2020), “The effect of non-performing loans on profitability of commercial banks: case of Vietnam”, Accounting, Vol. 6 No. 3, pp. 373-386, doi: 10.5267/ j.ac.2020.1.001. Duong, K.D., Tran, P.M.D., Nguyen, P.Y.N. and Pham, H. (2023), “How do funding diversity and nonperforming loans affect bank performance in different economic cycles?”, Cogent Business and Management, Vol. 10 No. 2, 2215076, doi: 10.1080/23311975.2023.2215076. El-Chaarani, H., Abraham, R. and Azzi, G. (2023a), “The role of liquidity creation in managing the COVID-19 banking crisis in selected mena countries”, International Journal of Financial Studies, Vol. 11 No. 1, p. 39, doi: 10.3390/ijfs11010039. EL-Chaarani, H., Skaf, Y., Roberto, F., Hamdan, A. and Binsaddig, R.O. (2023b), “Assessing the direct and moderating effect of COVID-19 on the performance of the banking sector in the MENA region”, FIIB Business Review, 23197145221137962, doi: 10.1177/23197145221137962. Elfergani, H.F., Abdelhamid Alzwi, A.S. and Zagoub, A. (2024), “The impact of capital adequacy and non-performing loans on loan loss provisions for Gulf cooperation council (GCC) banks: econometric study using panel data”, in Handbook of Banking and Finance in the MENA Region, pp. 245-286. AJEB 9,3 458 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 Erdas, M.L. and Ezanoglu, Z. (2022), “How do bank-specific factors impact non-performing loans: evidence from G20 countries”, Journal of Central Banking Theory and Practice, Vol. 11 No. 2, pp. 97-122, doi: 10.2478/jcbtp-2022-0015. Farn� e, M. and Vouldis, A. (2024), “Do retail-oriented banks have less non-performing loans?”, The Journal of Economic Asymmetries, Vol. 29, e00358, doi: 10.1016/j.jeca.2024.e00358. Foglia, M. (2022), “Non-performing loans and macroeconomics factors: the Italian case”, Risks, Vol. 10 No. 1, p. 21, doi: 10.3390/risks10010021. Gamba-Santamaria, S., Melo-Velandia, L.F. and Orozco-Vanegas, C. (2024), “Decomposition of nonperforming loans dynamics into a debt-servicing capacity and a risk taking indicators”, The Quarterly Review of Economics and Finance, Vol. 96, 101860, doi: 10.1016/j.qref.2024.04.007. Giammanco, M.D., Gitto, L. and Ofria, F. (2023), “Government failures and non-performing loans in Asian countries”, Journal of Economic Studies, Vol. 50 No. 6, pp. 1158-1170, doi: 10.1108/jes06-2022-0348. Gjeçi, A., Marin� c, M. and Rant, V. (2023), “Non-performing loans and bank lending behaviour”, Risk Management, Vol. 25 No. 1, p. 7, doi: 10.1057/s41283-022-00111-z. Hakimi, A., Boussaada, R. and Karmani, M. (2022), “Is the relationship between corruption, government stability and non-performing loans non-linear? A threshold analysis for the MENA region”, International Journal of Finance and Economics, Vol. 27 No. 4, pp. 4383-4398, doi: 10.1002/ijfe.2377. Hakimi, A., Boussaada, R. and Karmani, M. (2024), “Financial inclusion and non-performing loans in MENA region: the moderating role of board characteristics”, Applied Economics, Vol. 56 No. 24, pp. 2900-2914, doi: 10.1080/00036846.2023.2203456. Hernawati, E., Hadi, A.R.A., Aspiranti, T. and Rehan, R. (2021), “Non-performing financing among islamic banks in asia-pacific region”, Cuadernos de Econom� ıa, Vol. 44 No. 126, pp. 1-9. Huljak, I., Martin, R., Moccero, D. and Pancaro, C. (2022), “Do non-performing loans matter for bank lending and the business cycle in euro area countries?”, Journal of Applied Economics, Vol. 25 No. 1, pp. 1050-1080, doi: 10.1080/15140326.2022.2094668. Iqbal, S. and Nosheen, S. (2023), “Moderating impact of non-performing loans on the relationship between sustainable development goals and the financial performance of banks”, Future Business Journal, Vol. 9 No. 1, p. 46, doi: 10.1186/s43093-023-00224-1. Iqbal, J. and Saeed, A. (2023), “Managerial sentiments, non-performing loans, and banks financial performance: a causal mediation approach”, Chaos, Solitons and Fractals, Vol. 171, 113425, doi: 10.1016/j.chaos.2023.113425. Isayev, M. and Farooq, O. (2024), “Shadow banking and non-performing loans: international evidence”, Journal of Financial Regulation and Compliance, Vol. 32 No. 2, pp. 168-183, doi: 10.1108/jfrc-07-2023-0105. Kalu, E.U., Arize, A.C., Malindretos, J., Awa, K.I. and Eze, C.G. (2021), “Linear and asymmetric analyses of macro-economic and bank-specific determinants of non-performing loans in West African Monetary Zone (WAMZ)”, World Review of Entrepreneurship Management and Sustainable Development, Vol. 17 No. 5, pp. 670-691, doi: 10.1504/wremsd.2021.117447. Kanoujiya, J., Bhimavarapu, V.M. and Rastogi, S. (2023), “Banks in India: a balancing act between profitability, regulation and NPA”, Vision, Vol. 27 No. 5, pp. 650-660, doi: 10.1177/ 09722629211034417. Karadima, M. and Louri, H. (2020), “Non-performing loans in the euro area: does bank market power matter?”, International Review of Financial Analysis, Vol. 72, 101593, doi: 10.1016/ j.irfa.2020.101593. Karadima, M. and Louri, H. (2021), “Economic policy uncertainty and non-performing loans: the moderating role of bank concentration”, Finance Research Letters, Vol. 38, 101458, doi: 10.1016/j.frl.2020.101458. Kartika, I., Sulistyowati, S., Septiawan, B. and Indriastuti, M. (2022), “Corporate governance and nonperforming loans: the mediating role of financial performance”, Cogent Business and Management, Vol. 9 No. 1, 2126123, doi: 10.1080/23311975.2022.2126123. Asian Journal of Economics and Banking 459 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025 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, Vol. 5 No. 1, pp. 135-145, doi: 10.1108/ajar-10-2019-0080. Kim Quoc Trung, N. (2022), “Does leverage fit non-performing loans in the COVID-19 pandemic–evidence from the Vietnamese banking system”, Cogent Business and Management, Vol. 9 No. 1, 2119675, doi: 10.1080/23311975.2022.2119675. Kjosevski, J. and Petkovski, M. (2021), “Macroeconomic and bank-specific determinants of nonperforming loans: the case of baltic states”, Empirica, Vol. 48 No. 4, pp. 1009-1028, doi: 10.1007/s10663-020-09491-5. Kryzanowski, L., Liu, J. and Zhang, J. (2023), “Effect of COVID-19 on non-performing loans in China”, Finance Research Letters, Vol. 52, 103372, doi: 10.1016/j.frl.2022.103372. Kulu, E. and Osei, B. (2024), “Drivers of financial stability gap: evidence from sub-Saharan Africa”, Journal of Financial Economic Policy, Vol. 16 No. 1, pp. 19-33, doi: 10.1108/jfep-072023-0184. Kumar, M., Al-Romaihi, M.A. and Aktan, B. (2023), “Do the macro and global economic factors drive the nonperforming loans in GCC economies?”, Journal of Financial Economic Policy, Vol. 15 No. 3, pp. 190-207, doi: 10.1108/jfep-12-2022-0290. Kumari, S., Malpani, G., Mehendale, S. and Dadhich, M. (2024), “Effect of credit risk on profitability of Indian commercial banks: a panel data approach in a post-covid scenario”, in Pandemic to Endemic, Routledge, pp. 322-335. Lawrence, B., Doorasamy, M. and Sarpong, P. (2024), “The impact of credit risk on performance: a case of South African commercial banks”, Global Business Review, Vol. 25 No. 2_suppl, pp. S151-S164, doi: 10.1177/0972150920969927. Lee, Y.Y., Dato Haji Yahya, M.H., Habibullah, M.S. and Mohd Ashhari, Z. (2020), “Non-performing loans in European Union: country governance dimensions”, Journal of Financial Economic Policy, Vol. 12 No. 2, pp. 209-226, doi: 10.1108/jfep-01-2019-0027. Liu, S., Jin, J. and Nainar, K. (2023), “Does ESG performance reduce banks’ nonperforming loans?”, Finance Research Letters, Vol. 55, 103859, doi: 10.1016/j.frl.2023.103859. Loang, O.K., Ahmad, Z. and Naveenan, R.V. (2023), “Non-performing loans, macroeconomic and bank-specific variables in Southeast Asia during COVID-19 pandemic”, Singapore Economic Review, Vol. 68 No. 03, pp. 941-961, doi: 10.1142/s0217590822500679. L� opez-Espinosa, G., Ormazabal, G. and Sakasai, Y. (2021), “Switching from incurred to expected loan loss provisioning: early evidence”, Journal of Accounting Research, Vol. 59 No. 3, pp. 757-804, doi: 10.1111/1475-679x.12354. Mahrous, S.N., Samak, N. and Abdelsalam, M.A.M. (2020), “The effect of monetary policy on credit risk: evidence from the MENA region countries”, Review of Economics and Political Science, Vol. 5 No. 4, pp. 289-304, doi: 10.1108/reps-07-2019-0099. Mamoon, A., Kwabi, F., Ezeani, E. and Hu, W. (2025), “The impact of central bank independence and transparency on banks’ non-performing loans and economic stability”, Journal of Banking Regulation, Vol. 26 No. 1, pp. 25-40, doi: 10.1057/s41261-024-00237-y. Manz, F. (2019), “Determinants of non-performing loans: what do we know? A systematic review and avenues for future research”, Management Review Quarterly, Vol. 69 No. 4, pp. 351-389, doi: 10.1007/s11301-019-00156-7. Mohamad, A. and Jenkins, H. (2021), “Corruption and banks’ non-performing loans: empirical evidence from MENA countries”, Macroeconomics and Finance in Emerging Market Economies, Vol. 14 No. 3, pp. 308-321, doi: 10.1080/17520843.2020.1842478. Msomi, T.S. (2022), “Factors affecting non-performing loans in commercial banks of selected West African countries”, Banks and Bank Systems, Vol. 17 No. 1, pp. 1-12, doi: 10.21511/bbs.17(1) .2022.01. Nathan, S., Ibrahim, M. and Tom, M. (2020), “Determinants of non-performing loans in Uganda’s commercial banking sector”, African Journal of Economic Review, Vol. 8 No. 1, pp. 26-47. AJEB 9,3 460 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/3/437/10762429/ajeb-09-2024-0103en.pdf by ZBW German National Library of Economics user on 16 December 2025