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The increasing digitalization of financial services in the United States has introduced significant challenges in combating financial crimes, including fraud, money laundering, and cybercrimes. As digital transactions become more pervasive, so too do the opportunities for illicit activities that exploit the vulnerabilities of these systems. This research seeks to establish new benchmarks for combating financial crimes and ensuring the security of America's digital financial landscape. By leveraging advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and blockchain, this study explores innovative approaches to enhancing fraud detection, improving transparency, and reducing the risk of money laundering. A mixed-methods approach was employed, combining qualitative analysis of existing regulatory frameworks with quantitative case studies to assess the effectiveness of these technologies in real-world applications. The results indicate that AI and ML significantly enhance financial institutions' ability to detect fraudulent activities in real-time, reducing false positives and allowing for more efficient resource allocation. Furthermore, blockchain technology improved financial transactions' traceability, enhancing financial systems' transparency and accountability. The study concludes with recommendations for policy and practice, emphasizing the need for adaptive regulatory frameworks, increased investment in advanced technologies, and more vital collaboration between the public and private sectors. These findings suggest that a proactive approach, integrating technological innovation with strategic policy measures, is crucial for safeguarding America's financial infrastructure against emerging threats in the digital era.
Cedrick Agorbia-Atta (Mon,) studied this question.