Estimating the Scale of Illicit Financial Flows: The Abnormality Method

Daniel Coll Sol (), Mario Cuenda Garcia (), Bathusi Gabanatlhong (), Miroslav Palansky and Tijmen Tuinsma ()
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Daniel Coll Sol: Tax Justice Network, London, United Kingdom
Mario Cuenda Garcia: Tax Justice Network, London, United Kingdom
Bathusi Gabanatlhong: Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague,
Tijmen Tuinsma: Tax Justice Network, London, United Kingdom

No 2026/08, Working Papers IES from Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies

Abstract: This paper introduces the abnormality method to estimate illicit financial flows (IFFs) using a bilateral gravity model complemented by a machine learning technique to analyse unexplained financial flows to offshore centres. The findings provide robust evidence linking abnormal flows to offshore financial centres with tax avoidance and evasion and offer new estimates of their scale, costs, and geographical distribution. In 2023, abnormal flows to tax havens and secrecy jurisdictions reached US$2.8 trillion, resulting in foregone tax revenues exceeding US$60 billion. These flows originated mainly from Europe, the Americas, and Asia, flowing mostly to European tax havens. Random Forest analysis confirms that tax haven and secrecy jurisdiction status are key determinants of abnormal financial flows. Furthermore, the analysis of the Automatic Exchange of Information (AEOI) regulation indicates an increase in abnormal flows held in secretive jurisdictions.

Keywords: Illicit Financial Flows; Offshore Financial Centres; Revenue Losses; Machine Learning; Automatic exchange of Information (search for similar items in EconPapers)
JEL-codes: C45 F21 F23 H26 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2026-05, Revised 2026-05
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