This research investigates whether banks that adopted new regulatory requirements earlier, such as Basel III, are more profitable, as well as more efficient, than banks that adopted these requirements later. In addition, all 138 banks are based in the G7 member countries, which are the most developed countries in the world. Also, banks are categorized into early and late adopters based on Basel III Leverage Ratio performance by using Fitch Connect. Moreover, profitability ratios, such as the Return on Equity, Return on Assets and efficiency ratio Operating Efficiency, were collected from Fitch Connect to analyze if early adopters were more profitable and efficient than the late adopters. Also, STATA is used to analyze descriptive statistics and a univariate analysis of both groups. Furthermore, the finding is that early adopters of the Basel III Leverage Ratio are not the more profitable or efficient firms compared to late adopters as anticipated. In addition, the results of early and late adopters do not differ that much in the analysis regarding profitability and efficiency ratios. This implies that it is not necessarily correct to assume that stricter regulation, such as Basel III, will negatively affect the profitability or efficiency of banks. In addition, these results are useful to regulators and policymakers of the G7 member countries for two reasons. Also, regulators can clearly see how banks are adopting new stricter regulation.

Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution and is increasingly significant for companies’ economic performance. Small and medium-sized enterprises (SMEs), the foundation of economic development in most national economies, face numerous challenges and opportunities in applying artificial intelligence in business. This paper aims to examine the economic effects of applying artificial intelligence in SMEs, with a special emphasis on labor productivity, business process efficiency, and reduced operating costs. Empirical research was conducted on a sample of 228 SMEs using a questionnaire, with the data analyzed using multiple linear regression. The research results show that different applications of artificial intelligence have a statistically significant, positive impact on labor productivity and on reducing operating costs. In contrast, their impact on business process efficiency is moderate and partially limited. The operational application of artificial intelligence, such as automation and data analysis, has proven to be the most important factor in economic effects. At the same time, its application in managerial decision-making also has a significant, but somewhat weaker impact. On the other hand, the mere growth of AI applications over time does not necessarily lead to increased efficiency without targeted and concrete implementation. The paper’s results contribute to understanding the role of AI in transforming SMEs and highlight the importance of targeted investments in operational and management applications of AI. The paper provides practical implications for entrepreneurs and economic policymakers in fostering sustainable, competitive development of SMEs.

Purpose: Rapid technological advancement has accelerated the integration of financial technology (FinTech) into traditional banking systems. Banks have adopted digital payments, artificial intelligence, blockchain solutions and open banking frameworks, thereby increasing competition and prompting regulatory adaptation. This study conducts a theory guided systematic literature review and bibliometric analysis of FinTech banking research (2019–2024) to map the intellectual structure, thematic evolution and research gaps. Design/methodology/approach: The review analyses 224 peer reviewed journal articles indexed in the Web of Science Core Collection. Using BibExcel and VOSviewer, the study employs co-citation analysis, keyword co-occurrence mapping, clustering techniques and temporal overlay analysis. The review protocol follows explicit search strings, inclusion criteria and screening procedures to enhance transparency and replicability. Findings: Six major thematic domains emerge: competition and risk-taking dynamics, financial inclusion and regulatory boundaries, institutional technology integration, performance and efficiency outcomes, innovation and regulatory economics and digital transformation and adoption behaviour. Temporal analysis reveals a progression from adoption focused inquiry toward governance, competition and systemic stability debates. Despite increasing empirical sophistication, the field remains fragmented across behavioural, institutional and macroprudential levels. Originality/value: This study embeds bibliometric mapping within a multi-level theoretical framework integrating diffusion, disruptive innovation and ecosystem perspectives. The research provides a critical synthesis of the evolving FinTech banking literature. The findings identify key research gaps, reveal emerging thematic patterns in FinTech banking research and outline directions for future research while offering implications for banking practitioners and regulators.