To fight inflation, European Central Bank (ECB) announced 10 successive interest rate hikes, starting on 27 July 2022, igniting an unprecedented widening of interest rate spreads in the euro area (ΕΑ). Greek banks, however, recorded among the highest interest rate spreads, far exceeding ΕΑ median and weighted average. Indeed, we document a strong asymmetric response of Greek banks to ECB interest rate hikes, with loan interest rates rising immediately, whilst deposit interest rates remained initially unchanged and then rose sluggishly. As a result, the interest rate spread hit one historical record after another. Greek systemic banks, probably taking advantage of the high concentration and low competition in the domestic sector benefited from key ECB interest rate hikes, recording gigantic increases in net interest income (NII), and consequently, substantial profits (almost €7.4 billion in the 2022–2023 biennium). Such excessive accumulation of profits (that deteriorates the living conditions of consumers) by the banking system could be called the inflation of “banking greed”, or bankflation. This new source of inflation created by the oligopolistic structure of the Greek banking sector counterworks the very reason for ECB interest rate increases and requires certain policy analysis recommendations in coping with it.

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.

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.

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.

Private banking institutions serve the financial sector’s wealthiest clientele via a dedicated value proposition. Based on the relevant tendencies and statistics, a remarkable expansion can be outlined since the mid-1990s. The aim of this study is to elaborate the Hungarian private banking market’s development as a case study. The paper also intends to add to the literature on this unique segment of the financial market. Based on the available statistics, the analysis primarily focuses on the Hungarian private banking market’s rapid development process. This can be underpinned by the clientele’s savings, number of accounts and respective segmentation limits of the institutions. Referring to the amount of savings, a correlation analysis indicates significant co-movements with specific social and economic variables. The growth rate of the Hungarian clientele’s savings outperformed the respective indicator in Western Europe during the review time period (2007–2020). The current paper also includes a section that summarises general challenges that private banking managers need to address during the development process. Generally, the literature on private banking can still be considered scarce, whereas there is a lack of studies on the Central-Eastern European region. The analysis of the Hungarian sector’s development path can serve with relevant information to any financial expert in the field.

Loans are a critical transmission channel for commercial banks as well as an important revenue source. Macroeconomic factors are not within the control of commercial banks, however, select factors are observed to have a direct impact on lending behaviour in studies around the world. This study examined the relationship between macroeconomic variables and the lending behaviour of banks in South Africa for the period ranging from 2001 to 2022. Quarterly time series data was employed using the Autoregressive Distributed Lag Model (ARDL). The empirical results of the paper revealed that there is a long-run relationship between the repurchase rate (repo rate), inflation, the real effective exchange rate (REER) and lending behaviour in South Africa. The REER and inflation were both found to have a positive relationship, whilst the repo rate had a negative relationship. In addition, Gross Domestic Product (GDP), the activity rate and sovereign credit rating (SCR) changes returned insignificant results. Overall, these findings show that select macroeconomic factors do influence lending behaviour in South Africa. Furthermore, the results suggest that monetary policy decisions have a direct influential effect on lending and the South African Reserve Bank (SARB) has implemented their policies effectively.