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Anti-Money Laundering

Anticipatory and Adaptive Anti-Money Laundering (A3ML)

Sift through financial transactions graphs for suspicious patterns, learn new patterns to anticipate future activities, and develop techniques to represent patterns of illicit financial behavior in a concise, machine-readable format that is also easily understood by human analysts.

Agency
Defense Advanced Research Projects Agency (DARPA)
Capabilities
AI Research & Innovation
01 / Customer Challenge

The challenge our customer brought us.

Legacy anti-money laundering (AML) relies on manual, reactive monitoring that fails against evolving adversarial Tactics, Techniques, and Procedures (TTPs), complex multi-layered transaction graphs, and strict data privacy regulations restricting inter-agency data sharing.

02 / Knexus Approach

What we designed and delivered.

Knexus engineered adaptive graph-mining and machine learning algorithms to automatically extract money-laundering subgraphs, infer emerging TTPs from sparse data, and compile threat patterns into concise, privacy-preserving representations.

03 / Mission Impact

The difference it made.

Precision & Scale: Automated multi-hop graph detection across massive datasets, drastically reducing false positives compared to traditional rule-based filters.
Predictive Adaptation: Identified adversarial TTP pivots early and successfully adapted models against red-team evasion techniques.
Privacy-Preserving Intelligence: Enabled cross-agency and commercial threat-sharing using structural TTP signatures without exposing raw financial transactions or PII.
Operational Delivery: Delivered a modular, TRL 4/5 prototype software engine for direct integration into national security financial intelligence workflows.

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