This study examines the key factors influencing Japanese investors' decisions to direct investment into Thailand. As Thailand has emerged as a significant destination for foreign direct investment (FDI) from Japan, understanding the underlying drivers behind these investment choices is crucial for both policymakers and business leaders. Utilizing a mixed-methods approach, the research combines quantitative analysis of investment data with qualitative insights from interviews with key stakeholders in the Japanese investment community. The findings reveal that economic stability, favorable trade policies, and strategic location are primary drivers for Japanese investors. Additionally, factors such as market potential, infrastructure quality, and political climate also play significant roles. This study provides valuable insights into the investment behavior of Japanese firms and offers recommendations for Thai policymakers to enhance their attractiveness as an investment destination.

The innovative business risks include cybersecurity and regulatory risks and therefore the expansion of the use of Artificial Intelligence and Business Intelligence technologies in the risk management processes has come as a result. This article examines the role of Advanced Intelligence and Business Intelligence in altering risk management paradigms in the context of the US Organizations with emphasis on operations management and decision-making sophistication enhancement and risk management in advance. To achieve the above objective, a cross-sectional survey of 200 risk management professionals drawn from various organizations is used to gather data on the level of AI/BI adoption, the benefits sought and the difficulties experienced.

A structured online survey was used to solicit data relating to themes like integrated AI/BI, perceived enhancements in risk, challenges like high costs and data and ethical issues of AI-decision making. Categorized variables were used to present the demographics of the respondents and Pearson correlation and regression analyses tests were used to compare the impact of AI/BI adoption with enhanced risk management results. Chi-Square tests were conducted to establish the significance of the differences in the adoption and challenges by industries as well as the size of organizations.

Organizations with optimally deployed AI/BI systems realize enhanced system effectiveness, increased speed of decision-making processes and improved ability to manage risks in an anticipatory manner. A significant positive correlation was established between these outcomes and the level of AI/BI integration with these outcomes supporting the disruptive promise of these technologies. However, the study also shows the following challenges to adoption, which are high costs of implementing the solutions, difficulty in handling big data and shortage of skilled personnel in a firm. Moreover, ethical issues remain critical, especially with reference to the levels of transparency with artificial intelligence alongside data protection for individuals’ information; a pressing issue of concern especially to the health and financial sectors.

This study prompts further attention to the advancement of efficient AI solutions at a large scale and the generation of a set of ethical norms to incorporate into risk management particularly with reference to AI use. In future research, more efforts should be devoted to investigating the effects that the use of AI and BI has on risk management practices after a longer period of time has elapsed, as well as on how the barriers explored in this research could be efficiently mitigated for organizations, especially those small ones.

This study tests whether financial‑performance metrics predict share prices of FTSE‑100 banks. Using a quarterly panel (2012‑2023), we estimate a regression of bank stock price on profitability (ROE), capital adequacy, and liquidity, controlling for size. Results indicate that ROE and capital adequacy are both statistically significant and positively related to share price, suggesting markets value earnings capacity and solvency credibility in UK banks. Liquidity adds little explanatory power once profitability and capital are accounted for, implying its informational value is muted under a credible lender‑of‑last‑resort regime. The findings reinforce the view that markets do not price ratio classes uniformly but assign weight to those signals that close the most consequential uncertainty in banks, the uncertainty is not about transactions but about survival and return persistence. The study contributes to post-crisis and post-pandemic financial-management literature by showing that the performance-price channel in FTSE banks is confidence-conditioned rather than mechanically ratio-driven. The implications are relevant for bank managers, prudential regulators and investors in high-regulation financial systems.