The objective of this research is to assess the current state of e-banking in Saudi Arabia. The banking industry is rapidly evolving to use e-banking as an efficient and appropriate tool for customer satisfaction. Traditional banks recommend online banking as a particular service to their customers in order to provide them with faster and better service. As a result of the rapid advancement of technology, banks have used e-banking and mobile banking to both accumulate users and conduct banking transactions. Nonetheless, the primary challenge with electronic banking is satisfying customers who use Internet banking. Thus, the current study seeks to determine what factors affect e-payment adoption with e-banking services. mobile banking, e-wallets, and e-banking, as well as the mediating role of customer trust, can drive e-payment adoption. We distributed the survey online and offline to a total of 336 participants. A convenience sampling technique was used; structure equation modeling (SEM), convergence and discriminant validity; and model fitness were achieved through Smart PLS 3. The findings have shown that mobile banking, e-banking, and e-wallets are three significant independent variables that mediate the role of customer trust in influencing e-payment adoption when using Internet banking services. They should emphasize trust-building activities, specifically in relation to the new ways of e-payment such as e-banking, m-payments, NFC, and e-proximity, which will further help reduce consumer perceptions of risk. The system developers should design user-friendly applications and e-payment apps to enhance consumers’ belief in using them for payment purposes over any Internet-enabled device. They should promptly respond to consumers in cases of failed e-payment transactions and be able to promptly demonstrate transparency in settling claims for such failed transactions. Future studies could benefit from implementing probability sampling to facilitate comparisons with non-probability sampling studies. This study selected responses from only Saudi Arabian adopters of mobile payment technology. We need to conduct research on non-adopters and analyze the results using the model we proposed in this study. Due to time and resource constraints, in depth research using a mixed-methods approach could not be conducted. Future studies can utilize a mixed-methods approach for further understanding.

Purpose: The major objective of this study is to measure the impact of various attributes, such as social attraction, physical attraction, and task attraction on para-social relationships. The study also seeks to measure how the para-social relationship mediates the association between the three attributes (above-mentioned) on perceived credibility and informational influence, and consumers’ intention to purchase banking products. Study design/methodology: PLS-SEM has been used as it is believed to be most suited for the study due to the multivariate non-normality in the data, and the small sample size. Data has been collected using the 5-point Likert scale from approximately 151 respondents, who were selected using the non-random sampling method based on purposive sampling coupled with convenience-based sampling. The data was collected from January 2023 to August 2023. Findings: Largely, the findings reveal that both social and physical attractions do have a positive impact on the para-social relationship, further leading to perceived credibility and informational influence. Notably, this perceived credibility and informational influence lead to consumers’ intentions to purchase banking products, albeit with the use of artificial intelligence-based chatbots and digital assistants. Originality: This is possibly among the first-ever studies extending the para-social theory for purchasing banking products and services using artificial intelligence-based chatbots and virtual assistants.

Overwhelming studies unanimously agreed that preservation of the environment is a central climax in the discourse of green banking. There is a growing interest in exploring green banking practices for fostering financial inclusion, economic growth and sustainable development as part of Vision 2030 in Saudi Arabia. There are insufficient studies that examine this in the context of Saudi Arabia. This study aims at exploring the potential of green banking in order to attain sustainable banking and financial inclusion in achieving vision 2030in the country. Qualitative content analysis is used as a methodology of the study. Data were gathered through different sources such as: Web of Science (WOS), related journals, newspapers, published references, research papers, library sources and environmental organizations reports. It is indicated that green banking initiatives can be instrumental in fostering sustainable economic and environmental development in the Kingdom. The paper highlighted various activities of green banking such as: renewable and clean energy, financing green agriculture/food security, high-quality infrastructure among others. Nonetheless, some impediments to the green banking practices such as: risks facing green banks, poor quality of financial services among others are also mentioned in this paper. The paper proffers solutions to the challenges impeding green banking practices. In conclusion, the financial and banking industries in Saudi Arabia has been proving reform of the sector through greening economy. It is there suggested that the stakeholders and policymakers should provide efficient and effective technical, operational legal frameworks for enhancing green economy in achieving Vision 2030 in the country.

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.

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.

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.