Cross-Border Finance Meets Autonomous ML: A Framework for Governance and Compliance
DOI:
https://doi.org/10.15662/IJEETR.2026.0804005Keywords:
Autonomous Machine Learning, Cross Border Financial Transactions, Real Time Decision Systems, Funds Flow Automation, Transaction Settlement Networks, Transaction Settlement NetworksKnow Your Customer Compliance, Anti Money Laundering Controls, Sanctions Screening Systems, Risk Based Role Allocation, Cross Jurisdiction Data Sharing, Data Regulatory Interoperability, Financial Crime Prevention, Autonomous Compliance Systems, Jurisdictional Risk Management, Real Time Information Exchange, Data Residency Constraints, Regulatory Technology Integration, Global Financial Governance, Machine Driven Risk AssessmentAbstract
Autonomous machine learning—defined as the use of a machine learning system's response (the result of an action taken by it) to enable further decision-making without human intervention—has profoundly impacted global finance. Such an impact is manifested in flows of funds for transactional business in products and services, financial exchanges, and financial assets across borders. A clearer separation appears between the actual originator of the transaction and the funds-handling entity in other jurisdictions that provide transaction settlement or routing. This also creates risk and compliance challenges associated with these cross-border transactions in that the funds-handling entities must deploy systems that cover Know Your Customer, Anti-Money Laundering, sanctions, and other applicable laws
Turning cross-border data-sharing needs into reality remains a challenge. For certain data-sharing needs that emerge in cross-border financial interactions involving autonomous machine learning, the risk-based allocation of roles has led to initiatives to explore building data-sharing agreements with specific jurisdictions. The principle is that a transaction flows through jurisdictions with lower-risk data-regulatory considerations, thus enabling real-time flow of information without having the data remain for long periods in the jurisdiction with stringent data-regulatory requirements
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