Regulating the Algorithmic Bloodhound: Modernizing US Financial Regulations for the AI Era of Counter-Terrorism

The issue of terrorist financing is a major threat to international stability, because rogue actors take advantage of vulnerabilities of financial systems to direct funds towards illegal operations. The U. The S. financial system, though highly regulated and robust, is still a prime target because of its complexity, size and accessibility. Structural frameworks of monitoring that are based on predetermined rules and thresholds are usually ineffective in identifying advanced ways of hiding the money through structuring, layering, and distributing the money on a variety of accounts. This study examines how artificial intelligence and data analytics can be used to enhance counter-terrorist financing actions by exposing concealed patterns of financial transactions. With the PaySim dataset, which is a massive synthetic dataset of mobile money transactions, the study examines how such fraudulent behavior, as a proxy of terrorist funding, can be identified using sophisticated computational methods. This data offers very useful transaction-specific information, such as account balances, the types of transactions, and red flags of suspicious behavior, allowing the creation of models that detect anomalies with the potential of being used to commit illegal financial transactions. It is shown that fraudsters have a concentrated history of fraudulent operations in transfer and cash-out operations and this is commonly concentrated around small to mid-value cycles that replicate real world methods of hiding larger amounts of money by making smaller and less noticeable transactions. Time-related and network analyses further indicate that fraudulent activities also create networks and chains that are reminiscent of terrorist funding. In addition to the technical discoveries, the study also considers the ethical issues of privacy, transparency, and equity in implementing AI in the financial systems. In general, the study indicates that AI-based data analysis can profoundly improve how financial institutions and regulators can identify and disrupt possible terrorist financing and offers an effective model towards improving national security without violating ethical principles in financial surveillance.