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Logistics, high-level corruption, drugs in paradise, and the need for global supply chain intelligence.

This blog post argues that while "Explainable AI" (XAI) is useful for validating why a machine learning algorithm flagged a specific transaction, it falls short of true adversary attribution in anti-money laundering (AML) efforts. XAI focuses on explaining model logic around disposable artifacts, like single transactions, but fails to identify the individuals or threat networks behind the activity or their underlying business models. Section 2’s Hybrid Threat Finance™ (HTF) framework addresses this by shifting the focus from individual transactions to persistent behavioral patterns and Tactics, Techniques, and Procedures (TTPs). By analyzing these behaviors within a hierarchical taxonomy, financial institutions can move beyond model transparency to achieve true attribution, identifying specific threat networks and generating actionable intelligence for law enforcement.