AI drives cross-border financial suspicion beyond legal boundaries
Becoming Suspicious Across Borders: Algorithmic Extraterritoriality and AI-Driven Financial Surveillance
Artificial Intelligence
Summary
Suspicion plays a key role in detecting illegal money activities before full proof is available. This paper explains how AI changes the way suspicion is created, moving from human judgement within clear legal areas to automated data checks crossing countries. The authors introduce the idea of 'algorithmic extraterritoriality,' meaning AI systems can flag suspicious actions anywhere if the data passes through their systems. This makes it harder to understand, challenge, or hold accountable these AI-based decisions because the judgement process is spread out and not clear.
What this means in practice
- •For financial compliance teams: Adapt monitoring processes to consider AI-driven suspicion emerging from international data flows beyond traditional jurisdictions.
- •For data privacy regulators: Develop frameworks to increase transparency and accountability in AI-supported cross-border financial surveillance systems.
A position paper. It proposes an approach and reports no results.
Authors
Georgios Pavlidis, Savvas Chatzichristofis, Eleni Gavriil
Abstract
Suspicion is an important, yet elusive concept in anti-money laundering and counter-terrorist financing (AML/CFT), which allows for intervention below the threshold of proof. In its traditional form, suspicion can be understood as a situated legal judgement by human actors within identifiable jurisdictions. It is argued that this understanding is no longer adequate. As artificial intelligence (AI) becomes an integral part of financial surveillance, suspicion is increasingly produced through data-driven processes. This transformation is epistemic, but also spatial. Since AI-driven financial surveillance operates through transnational data infrastructures, regulatory reach is less a matter of where conduct occurs than a question of whether such conduct becomes visible within data systems. This article develops the concept of algorithmic extraterritoriality, understood as a form of regulatory power mediated by data infrastructures rather than formal assertions of jurisdiction. Moreover, since individuals are increasingly constituted as datafied subjects of suspicion, they are rendered governable through dispersed and opaque processes of evaluation. This constitutes a challenge for accountability and contestability because suspicion becomes more difficult to locate, explain or contest.