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.

ESG (environmental, social and governance, a framework used to assess an organisation’s business practices and performance on various sustainability and ethical issues) and Digital Transformation (the process of using digital technologies to change a business’s operations, products and services by integrating digital solutions into all areas of the business, which can lead to cultural and technological changes) are emerging issues across different industries, including the banking field. There has been limited research focusing on exploring the linkages between ESG, Digital Transformation and Customer Behaviour in the banking area, especially within developing countries such as Vietnam. Based on this gap, this study analyses and assesses the role of Digital Transformation and ESG on customer behaviour towards brands in the banking sector in Ho Chi Minh City. The research employed the quantitative research methods with the combination of fundamental analytical methods such as statistics, Cronbach’s alpha reliability, Exploratory Factor Analysis (EFA), measurement models and Partial Least Squares Structural Equation Modelling (PLS-SEM). The analysis was based on survey data from 550 customers who are the commercial banks’ current customers and live in Ho Chi Minh City, yielding 514 valid responses. Using SPSS and SMART PLS software, the study provided notable results. Specifically: (1) The component factors of ESG, including Environmental Issues (EN), Social Issues (SO), Government Issues (GO) and Digital Transformation (DT), positively influence Customer Behaviour (CB); (2) The component factors of ESG, including Environmental Issues (EN), Social Issues (SO) and Government Issues (GO), play a mediating role in the relationship between Digital Transformation (DT) and Customer Behaviour (CB).

Customers are displaying heightened awareness and involvement in their banking arrangements, and they are actively assessing and remembering information to make informed decisions regarding the allocation of their financial resources towards environmental protection solutions such as clean energy, sustainable construction, climate change control and social protection. Based on the current theoretical gap of factors influencing customer satisfaction and thereby encouraging continued engagement in green finance initiatives, this study aims to identify the factors influencing customer satisfaction as a means of fostering greater participation in green finance amongst customers of commercial banks in Ho Chi Minh City. Using data from a survey of 479 individuals who are customers at commercial banks in Ho Chi Minh City, this study analyses and evaluates the impact of factors influencing customer satisfaction and the role of customer satisfaction in green finance continuance behaviour. Combining basic analysis techniques in quantitative research such as statistics, evaluation of Cronbach’s alpha reliability, exploratory factor analysis (EFA), measurement models and Partial Least Squares structural equation modelling (PLS-SEM) from SPSS and SMART PLS software. the results of this research indicate that: (1) Green Banking initiative (GB), Information Support (IS) and Emotional Support (ES) positively impact Customer Satisfaction (SA); (2) Customer Satisfaction (SA) positively impacts Green Finance Continuance Behaviour (GF).

This article examines how financial technology determines bank performance in different EU countries. The answer to that question would allow banks to choose their development policy. The paper focuses  on the main and most popular bank services that are linked to financial technology. A SWOT analysis of FinTech is also presented to show the benefits and drawbacks of FinTech. FinTech-based services are very diverse and are provided by financial  firms and banks alike. This paper looks at the financial technology provided by banks: internet usage (internet banking), number of ATMs, credit transfers in a country, percentage of the population in a country holding a debit or credit card and whether that population has received or made a digital payment. Using the multi-criteria assessment methods of CRITIC and EDAS, the authors analysed and compared the countries of the European Union and the financial technology used in them. As a result of the application of these methods, the EU countries under consideration were ranked in terms of the use of financial technology. Subsequently, three banks from different countries with different levels of the use of financial technology were selected for the study. For these banks, financial ratios of profitability were calculated to characterise their performance. Correlation and pairwise regression analyses between the banks’ profitability ratios and financial technology were used to assess the relationship and influence between these ratios. The main conclusion of the study focuses on the extent to which financial technology influences the performance of banks in the selected countries. It is likely that further research will try to take into account the size of the country’s population when analysing all financial technologies. Researchers also needed to find out what influence financial technologies have on the such financial indicators as operational efficiency (costs), financial stability, and capital adequacy.

This study examines how economic freedom and competition affect bank stability. We use data from 70 ASEAN-4 banks from 2007 to 2019 using the system generalized technique of moments. Results corroborate competition-fragility hypothesis. Market strength (or less competition) can boost bank stability. However, in the ASEAN-4 area, competition and bank stability have a non-linear relationship, suggesting that bank stability may decline after market strength exceeds a threshold. Financial and economic freedom also boosts bank stability. This implies banks in free financial and economic contexts are more stable. Banks with more market dominance in nations with more economic or financial autonomy may also be more unstable. The findings suggest that authorities should allow some competition and economic flexibility to keep banks stable. The study examined ASEAN-4 economic freedom’s effects empirically for the first time. It illuminates competitiveness and bank stability.