DARPA-SN-25-23.pdf
Posted: Dec. 18, 2024
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Overview
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Summary
This special notice is issued by the Defense Advanced Research Projects Agency (DARPA) regarding the forthcoming Anticipatory and Adaptive Anti-Money Laundering (A3ML) program.
The notice serves to inform potential stakeholders about DARPA's intent to explore innovative research areas aimed at transforming current anti-money laundering (AML) practices. The A3ML program, overseen by Program Manager David Dewhurst, seeks to replace traditional manual and reactive methods with advanced algorithmic techniques that can efficiently analyze financial transaction data to identify suspicious patterns and predict future illicit activities.
The background section highlights the critical need for improved AML strategies, citing the detrimental effects of money laundering on national security and global interests, including its links to North Korea's nuclear funding and Mexican drug cartels.
Current AML efforts are hindered by their reliance on labor-intensive analysis and limited human cognitive capabilities, which allow many laundering schemes to evade detection. The A3ML initiative aims to develop sophisticated algorithms capable of processing vast amounts of financial data, learning new patterns of illicit behavior, and presenting these findings in a format that is both machine-readable and easily interpretable by human analysts.
Additionally, the document mentions DARPACONNECT, a resource platform designed to assist potential performers in navigating DARPA's processes and improving their proposal success rates.
It is important to note that this special notice does not constitute a formal solicitation or request for information (RFI), and no submissions will be accepted in response to it. Interested parties are advised that this notice does not obligate DARPA to issue a solicitation in the future.
The notice serves to inform potential stakeholders about DARPA's intent to explore innovative research areas aimed at transforming current anti-money laundering (AML) practices. The A3ML program, overseen by Program Manager David Dewhurst, seeks to replace traditional manual and reactive methods with advanced algorithmic techniques that can efficiently analyze financial transaction data to identify suspicious patterns and predict future illicit activities.
The background section highlights the critical need for improved AML strategies, citing the detrimental effects of money laundering on national security and global interests, including its links to North Korea's nuclear funding and Mexican drug cartels.
Current AML efforts are hindered by their reliance on labor-intensive analysis and limited human cognitive capabilities, which allow many laundering schemes to evade detection. The A3ML initiative aims to develop sophisticated algorithms capable of processing vast amounts of financial data, learning new patterns of illicit behavior, and presenting these findings in a format that is both machine-readable and easily interpretable by human analysts.
Additionally, the document mentions DARPACONNECT, a resource platform designed to assist potential performers in navigating DARPA's processes and improving their proposal success rates.
It is important to note that this special notice does not constitute a formal solicitation or request for information (RFI), and no submissions will be accepted in response to it. Interested parties are advised that this notice does not obligate DARPA to issue a solicitation in the future.
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