The paper assesses the threshold at which climate change impacts banking system stability in selected Sub-Saharan economies by applying the panel threshold regression on data spanning 1996 to 2017. The study found that temperature reported a threshold of −0.7316 ℃. Further, precipitation had a threshold of 7.1646 mm, while the greenhouse gas threshold was 3.6680 GtCO₂eq. In addition, the climate change index recorded a threshold of −0.1751%. Overall, a non-linear relationship was established between climate change variables and banking system stability in selected Sub-Saharan economies. The study recommends that central banks and policymakers propagate the importance of climate change uncertainties and their threshold effects to banking sectors to ensure effective and stable banking system operations.

While extensive research has explored interconnectedness, volatility spillovers, and risk transmission across financial systems, the comparative dynamics between Islamic and conventional banks during crises, particularly in specific regions such as Saudi Arabia, are underexplored. This study investigates risk transmissions and contagion among banks operating in Islamic and conventional modes in the Kingdom of Saudi Arabia. Daily banking stock data spanning November 2018 to November 2023, encompassing two major crises—COVID-19 and the Russian-Ukraine war—were analyzed. Using the frequency TVP-VAR approach, the study reveals that average total connectedness for both banking groups exceeds 50%, with short-run risk transmission dominating over long-term effects. Graphical visualizations highlight time-varying connectedness, driven predominantly by short-run spillovers, with similar patterns observed in both Islamic and conventional banking networks. The main contribution of this paper is the insight that long-term investment strategies are crucial for mitigating potential risks in the Saudi banking system, given its limited diversification opportunities.

The ongoing dissemination of globalization and digitalization may suggest that personal relationships are becoming less crucial in the context of retail banking and financial services. In Hungary, in addition to private banking, which is associated with high income levels, personal banking also plays an important role. The objective of this study is to develop a model that can identify the factors that determine customer satisfaction and their relative importance. Furthermore, the aim is to incorporate gender and age as moderator variables to identify demographic differences in satisfaction. The analysis was conducted via a questionnaire survey in October to November 2023 employing a purposive sampling approach in a university environment, as the respondents are likely to possess the highest level of existing financial knowledge within this population. The 214 valid responses were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, with the objective of contributing to the development of theory in this field of study. The results demonstrate that perception (β = 0.519) and reliability (β = 0.253) collectively explained 51.8% of the variance in satisfaction. Moreover, the results indicate that perception accounts for 49.2% of the variance in reliability, suggesting the existence of an indirect effect on satisfaction. Therefore, the findings suggest that, despite the advent of digital banking, face to face service remains a pertinent concern in Hungary, and financial institutions should prioritize the factors that shape customer satisfaction. The study contributes to the literature and to the development of customer loyalty strategies for banks based on these findings.

This study explores the advancement of ethical practices and environmental sustainability in Thai banking through an in-depth case analysis of Siam Commercial Bank (SCB), the country’s first indigenous bank founded in 1907. SCB has significantly influenced ethical banking practices and sustainability initiatives. The research provides a unique comparative analysis of SCB’s ethical frameworks and sustainability policies, assessing their impact on key stakeholders, including customers, employees, the community, and the environment. Employing a qualitative case study methodology, this study utilizes secondary data from SCB’s reports and CSR documents, analyzed through thematic analysis and descriptive statistics. The findings reveal SCB’s substantial progress in aligning ethical considerations with environmental sustainability, contributing new insights into ethical decision-making processes and the balance between profit and responsibility. Recommendations are provided to enhance ethical and sustainable practices in banking, adding to the discourse on corporate responsibility, environmental stewardship, and sustainable development.

Purpose: The purpose of this paper is to explore the impact of Artificial Intelligence on the performance of Indian Banks in terms of financial metrics. The study focused specifically on the NIFTY Bank Index. The paper also advocates that a greater transparency in disclosing AI related information in a Bank’s annual report is required even if it is voluntary. Design/Methodology/Approach: The paper uses a mixed method approach where quantitative and qualitative analysis is combined. A dynamic panel data model is used to understand the impact of AI of Return on Equity (RoE) of 12 Indian Banks in the NIFTY Bank Index over a five-year period. In addition to that, Content analysis of annual reports of banks was conducted to examine AI related disclosure and transparency. Findings: The paper highlights that the integration of Artificial Intelligence (AI) significantly influences the financial performance of sample banks of India. Return on Equity the specific parameter positively influenced with adoption of AI. The profitability of banks is positively impacted by reduced errors and improved operational efficiency. The content analysis of annual reports of the banks indicates different approach for AI disclosure where some banks give detailed information and some are not transparent about AI initiatives. The findings suggest that a higher level of transparency could enhance confidence of all stakeholders. Theoretical Implications: The positive relation between adoption of AI and financial performance, specifically ROE, gives a foundation for academic research to explore the dynamics of emerging technology and financial systems. The study can be extended to explore the impact on other performance indicators in different sectors. Practical Implications: The findings of this study emphasize the importance of transparent AI related disclosures. A detailed reporting about integration of AI helps in enhanced stakeholders’ confidence in case of banking industry. The regulatory framework of banks may also consider making mandatory AI disclosure practices to ensure due accountability to maximize the benefits of AI in banking.