Countering Radicalisation Financing Through Financial Intelligence and Predictive Risk Analytics from A Security Governance Perspective

Abstract

The global counterterrorism financing (CTF) architecture faces a systemic gap. Whereas conventional Financial Intelligence Unit (FIU) frameworks and Anti-Money Laundering/Counter Financing of Terrorism (AML/CFT) regimes were designed to detect operational terrorist financing, the movement of funds toward imminent attacks, they cannot detect the antecedent layer: radicalisation financing. This paper develops a novel framework, the Predictive Security Governance (PSG) model, to address this governance deficit. Drawing from a systematic literature review with expansive sources spanning financial intelligence transformation, machine learning applications, security governance theory, and human rights jurisprudence, the paper puts forward the PSG model as a theoretical frame, shown through four real-world-ish cases. These include Islamic State crowdfunding, Hamas cryptocurrency channels, far-right online donation ecosystems, and South Asian hawala linked networks. The overall idea blends three mutually dependent dimensions, as they feed each other: Institutional Intelligence Fusion; Algorithmic Risk Profiling (rooted in a three layer predictive architecture), and Human Rights Anchored Governance (put into practice via a proportionality triage mechanism). A Bidirectional Analytical Model (BAM) specifies the recursive feedback between intelligence outputs and governance recalibration, including explicit boundary conditions governing BAM functionality in data-sparse and institutionally constrained contexts. Existing CTF frameworks, the FATF risk-based approach, Egmont intelligence-sharing protocols, and UN CTED rights guidance, address radicalisation financing phenomenologically but not governance-systemically; the PSG model is the first framework to integrate institutional, algorithmic, and normative dimensions for this specific domain.