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The study aims to evaluate the financial distress position of selected sample banks in India. The top 10 banks with the highest levels of gross non-performing assets (NPA) under both public and private sector ownerships have been chosen for the study. Application of the Altman Z-score model has been used to compare both ownership banks’ financial distress for five years from 2017 to 2021. Based on the study’s findings, it was found that private sector banks demonstrated better financial stability than their public sector counterparts. Specifically, the average Z-score of the selected sample banks was higher than the safe zone threshold of 2.9 during the study period.

Serious concerns about the stability of the international financial systems have arisen recently, resulting from the mounting inflation rates and the accompanying procedures to control them. Consequently, this study aims at examining empirically the impact of inflationary pressures/shocks on the stability of banking sectors.

The study adopts a dynamic GMM models and exploits a sample of 188 banks operating in 14 MENA economies, over the period 1999–2021.

This research finds that high inflation does indeed harm bank financial stability and deteriorates banks credit risk. Furthermore, the examination of the impact of interaction terms between inflation and bank-specific and institutional quality variables shows that better capitalisation levels, higher liquidity buffers, larger asset size, greater market power, foreign ownership and overall political stability, all can counterbalance the impact of inflationary pressures on MENA banks financial stability.

In addition to empirically revealing how inflationary shocks can deteriorate financial stability, the main novelty of this research is examining how the interactions between inflation on one hand, and bank-specific and institutional quality on the other, affect bank stability.

The purpose of this study is to investigate the link between corporate social responsibility (CSR) and the amount of trust customers have in Somali Islamic banks. Furthermore, the role of gender in CSR activities and Islamic bank clientele is evaluated.

Throughout February and March 2022, 410 clients of Islamic banks in Somalia were surveyed using a questionnaire. The partial least squares approach and the structural equation model are applied to examine the data.

Findings indicate that all variables of CSR activities, such as social product, social legal, social needs, social environment and social employees’ responsibility, are influential and significant predictors of trust in Islamic banks in Somalia. Gender inequalities moderate the relationship between social product, social needs, social environment, social employee and trust. Conversely, only social legal responsibility was unaffected by gender differences in Somalia regarding people’s trust in Islamic banks.

A sample from a developing country such as Somalia is useful for shedding light on the outcomes of consumers’ perceptions of and trust in businesses’ CSR in the developing world. Furthermore, this study contributes to knowledge regarding CSR and how it can help the Islamic banking industry. Its findings will be useful to policymakers and regulatory bodies in the banking industry in their efforts to improve CSR.

To the best of the authors’ knowledge, this study is the first empirical investigation of its kind about the understudied relationship among customer trust, CSR efforts and gender in Somalia context. Furthermore, it investigates how gender specifically moderates CSR in the Islamic banking sector in a developing country.

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

The global emphasis on sustainability is driving organizations to embrace financial technology (Fintech) solutions as a means of enhancing their sustainable performance. This study seeks to unveil the intermediary role played by green finance and competitiveness, along with the moderating impact of digital transformation (DT), in the intricate relationship between Fintech adoption and sustainable performance.

Drawing on existing literature, we construct a comprehensive conceptual framework to thoroughly analyse these interconnected variables. To empirical validate of our model, a dual structural equation modelling–artificial neural network) SEM–ANN approach was employed, adding a robust layer of validation to our study’s proposed framework. A sample of 438 banking employees in Pakistan was collected using a simple random sampling technique, with 411 samples deemed suitable for subsequent analysis. Initially, data scrutiny and hypothesis testing were carried out using Smart-PLS 4.0 and SPSS-23. Subsequently, the ANN technique was utilized to assess the importance of exogenous factors in forecasting endogenous factors.

The findings from this research underscore the direct and significant influence of Fintech adoption and DT on the sustainable performance of banks. Notably, green finance and competitiveness emerge as pivotal mediators, bridging the gap between Fintech adoption and sustainable performance. Moreover, DT emerges as a critical moderator, shaping the relationships between Fintech adoption and both green finance and competitiveness. The integration of the ANN approach enhances the SEM analysis, providing deeper insights and a more comprehensive understanding of the subject matter.

This study contributes to the enhanced comprehension of Fintech, green finance, competitiveness, DT and the sustainable performance of banks. Recognizing the importance of amalgamating Fintech adoption, green finance and transformational leadership becomes essential for elevating the sustainable performance of banks. The insights garnered from this study hold valuable implications for policymakers, practitioners and scholars aiming to enhance the sustainable performance of banks within the competitive business landscape.

The purpose of the study is to identify cybersecurity threats that hinder the adoption of digital banking and provide sustainable strategies to combat cybersecurity risks in the banking industry.

Systematic literature review guidelines were used to conduct a quantitative synthesis of empirical evidence regarding the impact of cybersecurity threats and risks on the adoption of digital banking.

A total of 84 studies were initially examined, and after applying the selection and eligibility criteria for this systematic review, 58 studies were included. These selected articles consistently identified identity theft, malware attacks, phishing and vishing as significant cybersecurity threats that hinder the adoption of digital banking.

With the country’s banking sector being new in this area, this study contributes to the scant literature on cyber security, which is mostly in need due to the myriad breaches that the industry has already suffered thus far.

This study examined how economic freedom and its related components, such as open markets, regulatory efficiency, rule of law and the size of government, affect bank risk behavior, focusing on the Japanese context.

The study employs a two-step GMM framework on the annual data of Japanese banks ranging from 2005 to 2020 to empirically test the hypotheses. Furthermore, we also use the ordinary least square method to ensure the robustness of our mainline findings.

The finding suggests that economic freedom increases the banks' risk-taking, thus making them fragile. The results also highlight that out of the four main subcomponents of economic freedom, regulatory efficiency and government size increase bank risk-taking, while the rule of law and open markets decrease banks' risk-taking. Additionally, we examine how the banks' specific characteristics affect the results by creating a subsample based on capitalization and liquidity ratios. Overall, the results are consistent with the baseline findings. Moreover, the results are robust to alternative proxy measures of risk.

The study's findings have several implications for regulators and policymakers. The results suggest that regulators and policymakers should reconsider their strategies for economic freedom to ensure that they promote stability in the banking system and reduce banks' risk-taking inclinations.

Although previous studies have examined the impact of economic freedom on bank stability and risk-taking, this study is the first to do so in the Japanese context, contributing to the literature by providing new insights and empirical evidence.

This paper aims to examine the relationship between board structure and risk-taking, exploring how this association is influenced by advanced technologies in the banking sector.

This study uses a panel sample of 22 Pakistani banks from 2011 to 2018. To test the authors’ hypothesis, the authors use regression analysis with two-way cluster robust standard errors. Further, the authors also check the robustness of the authors’ findings using alternate proxies of board structure and bank risk-taking behavior. To address endogeneity concerns, the authors use the two-stage least square technique.

In the era of the Fourth Industrial Revolution, Pakistani banks’ digitalization is modeled by the presence of Temenos-T24/Oracle as their core banking system (software providing end-to-end operational integration). Its interactional effect with corporate governance is evaluated to implicate informed risk-taking by the board as a result of improved information access and analysis. The authors find that board size has a positive association with risk-taking, and the use of modern technology reshapes this association in the banking sector.

The contribution of this paper is twofold. First, the impact of board structure on bank risk-taking has not been extensively researched in Pakistan – a highly volatile and unpredictable economy. Second, the evaluation of the role of technology on bank risk is being researched for the very first time – a uniqueness of this paper.

Financial technology (FinTech) is experiencing transformation because artificial intelligence has become the new norm to enrich the experiences of individuals in this modern era of technological advancement. The article utilizes the stimuli-organism-response (SOR) framework to investigate how individual attitudes and behavioral intentions influence the adoption of FinTech, particularly in mobile banking.

433 respondents participated in the self-administered survey to answer questions related to demographic profiles and items to assess the variables adopted in the conceptual framework. The study applied “partial least squares structural equation modeling” PLS-SEM to analyze the data.

A structural equation model indicates that perceived usefulness and ease of use significantly affect attitude and behavioral intention. Moreover, the outcomes show that perceived value and social influence significantly influence, while perceived risks and performance expectancy insignificantly affect behavioral intention. Further, the outcomes also confirm that attitude and behavioral intention substantially influence mobile banking adoption.

The article provides insights for practitioners to improve and assess the quality of mobile banking services by using proposed antecedents that may increase the actual use of FinTech services, which serves as a valuable resource for stakeholders.

The new research model adds to the existing literature by offering empirical evidence of mobile banking adoption by considering three theories. Further, the study builds upon the S-O-R framework that incorporates FinTech attributes to explain the antecedents of the actual use of FinTech towards mobile banking adoption.