Strengthening U.S. National Security through Machine Learning-Based Financial Crime Detection
DOI:
https://doi.org/10.54536/ajfti.v3i1.6154Keywords:
AML, Artificial Intelligence, Counter-Terrorism Financing, Financial Crime Detection, Fraud Prevention, Machine Learning, Risk Management, U.S. National SecurityAbstract
The traditional rules-based systems to prevent financial crime in the U.S. are ineffective against sophisticated new threats. They cannot keep up with emerging criminal techniques that take advantage of vulnerabilities in digital financial systems. This study explores the application of Machine Learning (ML) and Artificial Intelligence (AI) to U.S. national security. The method used was a literature review. This study considered near-term advances in the use of machine learning technology to detect threats and tackled regulation, inter-agency cooperation and conventional detection approaches within American financial institutions. The research demonstrates that AI systems outperform standard methods. Detection accuracy by AI systems is 92-97%. The study revealed that AI systems have a 60-80% reduction in false positives. These systems can handle more than 10,000 transactions per second in real time. The existing regulations, such as the Bank Secrecy Act, USA PATRIOT Act and FinCEN regulations, serve as a basis for these more sophisticated systems. Interagency operations using Section 314(a) programs and financial intelligence units: Over 2.3 million cases per year are processed. These AI systems have above 90% success rates. The research finds that machine learning takes financial crime detection from reactive to proactive and provides solutions that can scale and evolve with new threats. This is in the interest of national security as it makes the U.S. financial infrastructure and democratic institutions more secure.
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Copyright (c) 2025 Samuel Gyasi Adom, Benedicta Emefa Gokah, Lawrence Kofi Abakah, Eric Asamoah, Owolabi Babatunde Akinsanya
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