The objective of this paper is to assess the influence of various types of crises, including the Subprime, COVID-19, and political crises, on corporate governance attributes, regulations, and the association with bank risk. The consecutive occurrences of crises have significantly impacted the global economy, causing substantial disruptions across various facets of the international banking system. Our hypothesis posits that these crises not only influence governance characteristics and regulations but also impact their correlation with the risk and financial distress experienced by banks. Our study is conducted within the Tunisian context spanning from 2000 to 2021, utilizing a GMM regression on a dataset comprising 221 bank-year observations. Our findings indicate that crises have a discernible effect on the relationship between corporate governance and bank risk, as well as between regulation and bank risk. Our results are strong in a range of sensitivity checks, including the use of alternative proxies to measure the bank risks and corporate governance metrics.

The study aimed to demonstrate that Palestinian banks have the potential to increase green financing by enhancing public sector understanding instead of focusing solely on the private sector, in addition to providing insights from employees of Palestinian banks listed on the Palestine Stock Exchange regarding the key challenges and opportunities related to green financing in Palestine specifically. It posed two central questions: What are the opportunities and challenges in implementing green finance in Palestine, and what level of government and private sector support exists? The study used the descriptive analytical approach, through interviews and surveys, the study targeted 10 heads of credit departments and a non-probability sample of 350 bank employees. The findings revealed a strong commitment from the government to promote green finance. At the same time, the private sector showed reluctance to engage in external investments. Key challenges included political instability and limited financial resources, though international aid was a significant opportunity to advance green finance. The study recommended increasing public awareness and fostering stronger coordination between the government and private sector, possibly incorporating competition from neighboring countries to further develop Palestine’s green finance strategy.

In this study, we explore the impact of contemporary bank run incidents on stock market performance, taking into consideration insured deposit concentration. Specifically, we use data from the recent downfall of the Silicon Valley Bank (SVB). By employing event study methods with the mean-adjusted return model and market models, we evaluate the cumulative abnormal returns (CARs). Our findings reveal a substantial negative CAR for all the listed companies in our sample, suggesting that the SVB crisis adversely affected stock returns. Further analysis shows an even more pronounced effect on the banking sector and that banks with a high concentration of insured deposits experienced economically and statistically less negative CARs. We also find that the response by the Treasury Department, the Federal Reserve, the Federal Deposit Insurance Corporation, and other agencies—aimed at fully safeguard all depositors—led a rebound in CARs. Our results highlight the importance of deposit insurance policy and regulatory responses in protecting the financial system during panic events.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

This study examines the influence of organizational learning and boundary spanner agility in the bank agent business of Indonesia’s financial inclusion. This study is based on quantitative studies of 325 bank agents in Indonesia. The results of this research strongly show that organizational learning has a significant impact on boundary spanners’ agility to achieve both financial and non-financial performance. This study presents a novel finding that organization learning with a commitment to apply and encourage learning activities and agility with improved responsiveness and resilience boundary spanners can achieve bank agent performance. Organizational learning of bank agents needs to improve commitment to apply and encourage learning activities, always be open to new ideas, and create shared vision and knowledge transfer mechanisms. Organizational agility in bank agents need also to improve the capability to be more responsive and adaptable to culture changes in a volatile environment. This research provides valuable insights to policymakers, banking supervisors, bank top management teams, and researchers on the factors that may improve the effectiveness of the agency banking business to promote financial inclusion. Participating banks in the agent banking business need to set a clear vision, scope, and priority of strategy to encourage organizational learning and agility.

This article aims to measure and identify the factors influencing the decision to use Chatbot in e-banking services for GenZ customers in Vietnam through 292 customers. Testing methods: Cronbach’s Alpha trust factor, EFA discovery factor analysis, and regression analysis have shown that 07 factors directly affect GenZ’s decision to use Chatbot. Those factors include (1) Customer attitude; (2) Useful perception; (3) Perception of ease of use; (4) Behavioral control perception; (5) Risk perception; (6) Subjective norms and (7) Trust. On that basis, the article has set out management implications for Vietnamese commercial banks to approach and increase the decision of customers aged 18–24 years in Vietnam.