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AI financial surveillance challenges traditional suspicion concepts

By PulseAugur Editorial · [1 sources] ·

A new paper explores how AI-driven financial surveillance challenges traditional notions of suspicion within anti-money laundering and counter-terrorist financing frameworks. The research introduces the concept of "algorithmic extraterritoriality," where regulatory power is exerted through transnational data infrastructures rather than formal jurisdictional claims. This shift makes suspicion harder to locate, explain, and contest, as individuals are increasingly governed by dispersed and opaque data-driven evaluations. AI

IMPACT AI's role in financial surveillance raises new challenges for regulatory frameworks and accountability.

RANK_REASON The cluster contains an academic paper discussing AI's impact on financial surveillance and regulatory concepts. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI financial surveillance challenges traditional suspicion concepts

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The cluster contains an academic paper discussing AI's impact on financial surveillance and regulatory concepts. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Georgios Pavlidis, Savvas Chatzichristofis, Eleni Gavriil ·

    Becoming Suspicious Across Borders: Algorithmic Extraterritoriality and AI-Driven Financial Surveillance

    arXiv:2610.03425v1 Announce Type: new 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…

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