The importance of artificial intelligence in the banking industry is reflected in the speed at which financial institutions are adopting and implementing AI solutions to improve their services and adapt to new market demands. The aim of this research is to conduct a bibliometric analysis of the involvement of artificial intelligence in the banking sector to provide a comprehensive overview of the current state of research to guide future directions and support the sustainable development of this rapidly expanding field. Another important objective is to identify research gaps and underexplored areas in the field of artificial intelligence in banking. The methodology used is a bibliometric analysis using VOSviewer, analysing 1089 papers from the Web of Science database. The results of the study provide relevant information for banking professionals but also for policy makers. Thus, the study highlights key areas where banks are using artificial intelligence to gain competitive advantage, thereby guiding practitioners in strategic decision making. Moreover, by identifying emerging trends and patterns in AI adoption, the study helps banking practitioners with foresight, enabling them to anticipate and prepare for future developments in the field. In terms of governmental implications, the study can contribute to the development of more nuanced regulatory frameworks that effectively balance the promotion of AI innovation with the protection of ethical standards and consumer protection.

This study examines the impact of the European Union’s Common Agricultural Policy (CAP) funds, specifically the European Agricultural Fund for Rural Development (FEADR) and the European Agricultural Guarantee Fund (FEGA), on a range of economic, social, and environmental outcomes across European regions. Utilizing Fully Modified Ordinary Least Squares (FMOLS) estimators, this research analyses 13 equations corresponding to various dependent variables, including employment rates, poverty levels, agricultural productivity, and environmental indicators such as greenhouse gas emissions and renewable energy production from agriculture. This analysis covers the period from 2010 to 2021, and draws upon a balanced sample of 301 observations to ensure robust estimations. Results indicate that both FEADR and FEGA payments significantly influence these regional outcomes, though the effects vary depending on the specific economic or environmental indicator examined. The findings reveal that while FEADR payments positively impact rural employment, agricultural income, and renewable energy production, they are less effective in addressing poverty reduction and productivity enhancement. Conversely, FEGA payments exhibit a stronger influence on agricultural productivity and income, but have mixed effects on environmental sustainability. This study highlights significant regional disparities, suggesting that the allocation of CAP funds is uneven in its impact across regions. The implications for policymakers are clear: a more tailored approach is required to enhance the effectiveness of CAP funds in meeting diverse regional needs, particularly in promoting economic development while minimizing environmental harm. This study also emphasizes the need for further research to explore alternative policy mechanisms and innovative agricultural practices that can bridge the gaps identified in the current CAP framework. Limitations of this study include data availability and the inherent complexity of agricultural systems, which may affect the generalizability of the results across different EU member states.

This study investigates the relationship between artificial intelligence (AI), industrial robots, and renewable energy consumption, driven by the rapid technological advancements and widespread adoption of AI tools in various industries. This research aims to evaluate the environmental implications of these technologies, specifically their impact on renewable energy usage. Employing a comprehensive analytical framework, this study utilizes advanced methodologies, including regularization factors, to accurately estimate the effects of these variables. Through a thorough data analysis, the research quantifies how AI and industrial robots influence the shift towards renewable energy sources. The findings reveal that investments in AI significantly enhance renewable energy consumption, as demonstrated by both conventional estimation techniques and those that integrate regularization factors. Conversely, the use of industrial robots is found to have a detrimental effect on renewable energy consumption. These results have important implications for policymakers, industry leaders, and sustainability researchers. This study encourages policymakers and investors to prioritize funding for AI solutions that promote renewable energy adoption, while it advises industry managers to strategically modify their use of industrial robots to reduce their environmental impact. Ultimately, this research lays a critical foundation for future inquiries and policy initiatives aimed at aligning technological advancements with sustainable energy practices.

Research background: The ongoing digital transformation within the European Union's banking sector, driven by the adoption of financial technologies (FinTech) and artificial intelli-gence (AI), has introduced substantial opportunities for enhancing operational efficiency, customer service personalization, and financial outreach. However, these innovations also generate strategic complexity, intensify competition from non-bank digital entrants, and create disparities in adaptation capacity across financial institutions. Despite a growing body of literature on this topic, empirical investigations remain limited regarding how these technologies differentially affect banks, based on their existing performance levels. Purpose of the article: This study examines the extent to which FinTech and AI integration influences financial performance across banks in all 27 EU member states. Specifically, it investigates whether these technological drivers yield uniform effects or whether their impact varies across banks depending on their baseline performance. The analysis is grounded in the premise that FinTech and AI are not inherently per-formance-enhancing and that their effects may depend on context, capacity, and strategy. Methods: Employing a panel quantile regression model, the analysis is based on a balanced panel dataset spanning the 2017–2023 period. To address both short- and long-term dynamics, the study complements its core estimation with a vector error correction model (VECM) and validates the robustness of findings through sys-tem-based regression techniques. The econometric framework incorporates bank-specific instruments and lagged performance metrics, with a particular focus on return on equity (ROE) as the dependent variable. Findings & value added: The results indicate that the adoption of FinTech and AI is associated with signif-icant improvements in performance metrics, particularly among well-capitalized or technologically agile banks. However, institutions with weaker fundamentals may experience limited or even adverse effects. These findings suggest that the performance implications of innovation are conditional, not universal. The paper contributes by offering a distribution-sensitive analysis that refines our under-standing of technological transformation in EU banking and provides actionable insights into strategic decision-making and regulatory oversight. Also, the results offer practical implications for bank executives considering strategic technology investments, as well as for regulators aiming to design supportive, risk-sensitive digital finance policies.

Financial development is often associated with significant economic growth, but studies have shown that a high level of financial development can be the cause of deepening income inequality in many countries. The main objective of the proposed study is to identify to what extent financial development influences income inequality in Central and Eastern European Countries (CEEC). Thus, for the model specification we used as dependent variable the Gini coefficient and as independent variable the financial development index. The sample period for the analysis was from 2004 to 2019, restricted by the lack of data on the Gini coefficient in CEECs. Data on the financial development index were collected from International Monetary Fund, and data on the Gini coefficient were extracted from the World Bank’s Poverty and Inequality Platform. The study unravels several contributions. First of all, the use of quantile regression allowed for the examination of the effects of financial development across the entire distribution of income inequality. Second of all, the use of a comprehensive financial development index offered a more robust and comprehensive measure of financial development compared to single indicators. Taking into account that the Gini coefficient must be close to zero, this result was a positive one with, in essence, financial development reducing income inequality in CEECs. Thirdly, the specific focus on CEECs fills a gap in the literature. Finally, the findings of this study have important policy implications. The obtained results indicate a negative causal relationship between financial development and income inequality, emphasizing the fact that the relationship between these two components cannot be generalized for all regions. These might include measures to promote financial inclusion, improve financial literacy, and enhance the stability and efficiency of financial systems. Supporting financial development in CEECs and similar transition economies can be an effective strategy for tackling income inequality.

This study investigates the sector-specific economic impacts of robot density across countries with varying levels of technological adoption. The analysis focuses on three key sectors—manufacturing, industry (excluding construction), and construction—using panel data from 12 European Union countries between 2016 and 2022. To explore these relationships, the study employs the Method of Moments Quantile Regression (MMQR) methodology, which enables the assessment of the effects of robot density across different levels of sectoral performance while accounting for cross-country variations and heterogeneity. The findings reveal that robot density significantly enhances economic performance in the manufacturing sector, while its effects are smaller but still positive in the industrial and construction sectors. These results highlight the varying capacity of sectors and countries to integrate and benefit from automation technologies. The study concludes by emphasizing the importance of tailored automation strategies and policy interventions to maximize the economic benefits of robotics across diverse national and sectoral contexts. These insights contribute to understanding the role of automation in driving industrial transformation and economic growth.