Ireland Financial Sector Assessment Program-Technical Note on Anti-Money Laundering/Combating the Financing of Terrorism

While domestic money laundering (ML) threats are well understood by the authorities, Ireland faces significant and increasing threats from foreign criminal proceeds. As a growing international financial center,1 Ireland is exposed to inherent transnational money laundering and terrorist financing (M...

Full description

Bibliographic Details
Corporate Author: International Monetary Fund Monetary and Capital Markets Department
Format: eBook
Language:English
Published: Washington, D.C. International Monetary Fund 2022
Series:IMF Staff Country Reports
Subjects:
Online Access:
Collection: International Monetary Fund - Collection details see MPG.ReNa
Description
Summary:While domestic money laundering (ML) threats are well understood by the authorities, Ireland faces significant and increasing threats from foreign criminal proceeds. As a growing international financial center,1 Ireland is exposed to inherent transnational money laundering and terrorist financing (ML/TF) related risks. The ML risks facing Ireland include illicit proceeds from foreign crimes (e.g., corruption, tax crimes). Retail and international banks, trust and company service providers (TCSPs),2 lawyers, and accountants are medium to high-risk for ML, while virtual asset service providers (VASPs) pose emerging risks. Brexit, the recent move of international banks to Dublin, and the COVID-19 pandemic increased the money laundering risks faced by Ireland. The Central Bank of Ireland (Central Bank) nevertheless has demonstrated a deep and robust experience in assessing and understanding their domestic ML/TF risks; however, an increased focus on risks related to transnational illicit financial flows is required. A thematic risk assessment undertaken by the Anti-Money Laundering Steering Committee (AMLSC) of international ML/TF risks would enhance the authorities’ risk understanding and is key to effective response to the rapid financial sector growth. Introducing data analytics tools, including machine learning to leverage potentially available big data on cross-border payments, would allow for efficient detection of emerging risks. The results of this assessment should be published to improve the understanding of transnational ML/TF risks and feed into the anti-money laundering and combating the financing of terrorism (AML/CFT) policy priorities going forward
Physical Description:18 pages
ISBN:9798400222795