Asian Journal of Research in Computer Science

This study explores how integrating blockchain technology with artificial intelligence can strengthen trust, transparency, and regulatory compliance in cross-border financial transactions. Drawing on open-source international datasets and quantitative analysis, the research shows that transaction costs rise as the number of intermediaries increases, while greater transparency significantly lowers these costs. Using transaction data from the Elliptic AML dataset, AI-based models achieved a fraud detection accuracy of 90.1%, demonstrating strong potential for identifying illicit financial activity. Structural equation modelling based on Financial Action Task Force indicators confirms that transparency and AI-driven risk detection jointly enhance compliance efficiency. To assess system-wide impact, a simulation using Bank for International Settlements cross-border statistics indicates that an integrated approach can reduce settlement times by over one-third and substantially improve data accuracy. Building on these findings, the study proposes the Blockchain–AI Trust and Transparency Framework (B-AITTF) as a practical reference model for financial institutions and regulators seeking to implement transparent, data-driven, and interoperable compliance mechanisms in cross-border finance, addressing the persistent fragmentation, opacity, and regulatory misalignment that continue to undermine trust and efficiency in existing cross-border payment infrastructures.