Economic policy uncertainty (EPU) adversely affects financial system functioning with potentially critical repercussions for economies and corporations worldwide. Financial system efficiency (FSE) has a vital influence on fostering optimal economic growth and development; however, the impact of EPU on FSE remains under-explored. This study investigates the effect of EPU on FSE along with its components financial institution efficiency (FIE) and financial market efficiency (FME). Using data from 22 countries over a 20-year period (2002–2021), our analysis reveals a significant negative effect of EPU on FSE, FIE and FME. Notably, our split-sample analysis highlights the accentuated adverse effects of EPU in high-EPU regimes, emphasising the importance of vigilance during periods of elevated policy uncertainty. We conduct a series of sensitivity tests, including alternative measures of EPU, FSE, FIE and FME, and apply two-stage least squares and two-step dynamic system generalised method of moments estimators and introduce additional control variables. These tests consistently reaffirm the core conclusions of our study. Finally, we discuss the implications of our findings for policymakers.

This study provides a theoretical argument supported by empirical evidence regarding the impact of sustainable investment practices on the working capital management of small- and medium-sized enterprises (SMEs). The study utilizes panel data on SMEs from 2014 to 2022. Cluster regression methodology is employed to assess the influence of sustainable investment practices on a firm’s working capital, with green finance (GF) as a moderating variable. Results revealed that a firm’s sustainable investment practices significantly and negatively affect its working capital. Additionally, the study identified GF as a moderator in the relation between sustainability and working capital. The findings indicated that firms actively engaging in sustainable investment practices are more efficient in managing working capital for their business operations’ long-term sustainability.

This study investigates the evolving role of artificial intelligence (AI) in enhancing cybersecurity risk management within fintech platforms. It focuses on how AI-driven systems impact threat detection capabilities, regulatory compliance, and algorithmic transparency in online financial transactions. A quantitative research design was employed, analyzing cybersecurity and privacy compliance data from 15 fintech platforms across North America, Europe, and Southeast Asia. Data sources included public audits, white papers, and platform-level documentation. The study tested three hypotheses concerning AI's impact on detection accuracy, the trade-off between model complexity and explainability, and the effectiveness of privacy-by-design in achieving GDPR compliance. The results show that AI-driven systems enhance threat detection accuracy by an average of 10% over traditional rule-based methods. However, increased model complexity significantly reduces explainability, posing challenges for regulatory accountability. Platforms adopting privacy-by-design principles consistently demonstrate stronger GDPR compliance and fewer security breaches. AI significantly strengthens fintech cybersecurity performance, but it introduces critical governance challenges related to transparency and data privacy. A balanced approach integrating explainable AI and privacy engineering is essential for sustainable innovation in the sector. The findings underscore the need for platform-specific risk management models that prioritize both technical performance and ethical design. Developers and compliance teams should embed governance protocols and privacy protections into AI system architecture from inception to ensure operational resilience and regulatory readiness.

Financial technology (FinTech) is transforming wealth management through robo-advisory services, leveraging artificial intelligence for cost-effective financial guidance. However, cultural values and financial literacy significantly influence adoption, particularly in emerging markets. This study examines the role of cultural perceptions, financial literacy, trust in AI, and perceived risk in robo-advisory adoption using Structural Equation Modeling (SEM) on data from 380 UAE respondents. The findings confirm that individualism and low uncertainty avoidance foster adoption, whereas collectivist and high uncertainty avoidance cultures exhibit reluctance. Financial literacy enhances adoption but does not moderate cultural influences, indicating the persistence of cultural norms despite financial knowledge. Trust in AI boosts adoption, while perceived risk deters engagement. These insights emphasize the need for culturally adaptive FinTech solutions, trust-building measures, and regulatory enhancements in AI transparency and cybersecurity. Future research should explore financial behavior trends and cross-cultural comparisons to advance global digital financial inclusion.

This study investigates the influence of robo-advisors on financial literacy and systematic investment behavior among retail investors in the United Arab Emirates (UAE). It aims to evaluate whether the adoption of robo-advisory platforms translates into improved investor knowledge and disciplined financial behavior. A descriptive-correlational research design was employed, integrating both quantitative and qualitative methods. Data were collected through a structured survey administered to 150 retail investors in the UAE. The responses were analyzed using correlation analysis and thematic qualitative techniques to identify key patterns and relationships. The analysis reveals that financial literacy is a significant predictor of systematic investment behavior, whereas robo-advisor usage alone does not result in statistically significant improvements. Correlation results indicate stronger associations between investment discipline and both income level and financial literacy, compared to technology adoption. Thematic analysis highlights that trust, ease of use, and limited human interaction are critical factors shaping investor perceptions of robo-advisory services. Robo-advisory platforms, while beneficial in terms of accessibility and cost-effectiveness, do not inherently foster improved financial behavior. Effective behavior change requires more than technological solutions and should incorporate features that support cognitive engagement and learning. The findings underscore the importance of integrating behavioral reinforcement tools such as financial education modules, personalized nudges, and feedback mechanisms into robo-advisory platforms. Financial technology developers and policymakers should focus on holistic design strategies that enhance both investor knowledge and behavior.