This paper presents a comparative review of the literature on foreign exchange rate forecasting, with focus on whether artificial intelligence methods can improve prediction results when compared with classical econometric models. Recent research has moved from theory-based statistical models toward data-based methods that are able to identify nonlinear patterns and use both numerical market data and information from financial texts. However, the results of these studies are difficult to compare directly because they use different currency pairs, time periods, forecasting horizons, input variables, and evaluation methods. This makes it harder to draw a clear conclusion about how large and stable the reported improvements are. The review examines fifteen peer-reviewed studies indexed in Web of Science, most published between 2019 and 2025. The studies are grouped into four categories: classical econometric models, machine learning models, deep learning models, and approaches connected with large language models. Based on document review and qualitative comparison, the paper analyzes whether artificial intelligence methods perform better than econometric benchmarks in out-of-sample forecasting. It also looks at the main design choices that may explain better forecasting results. The evidence reviewed in this paper suggests that artificial intelligence models often achieve better results than classical econometric models on common forecasting measures such as root mean squared error, mean absolute error, and directional accuracy. Deep learning models, especially long short-term memory variants and attention-based models, tend to show stronger performance in several studies. The review also shows that models using financial text may add useful information that is difficult to include in standard econometric models. The paper brings together findings from different strands of the literature and shows where the improvements are more visible. At the same time, it points out several remaining problems, especially model interpretation, stability across different market conditions, and practical use in real forecasting settings.

The rapid digitalization of financial banking services has increased the need for effective cybersecurity risk mitigation as institutions rely on interconnected digital infrastructures. Understanding how technological, organizational, and human-related factors interact is important for supporting cybersecurity planning and risk management. This study examines the internal structure of cybersecurity risk mitigation in financial banking systems through a framework that combines bibliometric analysis, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method, and the Matrix Impact Cross-reference Multiplication Applied to Classification (MICMAC) technique. The bibliometric analysis identified the factors most frequently associated with cybersecurity risk mitigation in scientific publications indexed in the Web of Science Core Collection. These factors were subsequently evaluated by a panel of IT and cybersecurity experts from the banking sector. DEMATEL was applied to examine the influence relationships among the identified factors, and MICMAC was used to refine the system by classifying the factors according to their driving and dependence power. The analysis provides a structured representation of the cybersecurity risk mitigation system and clarifies the role of each factor within the overall structure. The proposed framework may support managers, cybersecurity specialists and decision makers in cybersecurity planning, resource allocation, and strategic decision making within financial institutions.