Abstract
Purpose
This paper aims to critically examine the digital transformation of anti-money laundering (AML) and countering the financing of terrorism (CFT) in light of the Financial Action Task Force (FATF) San Jose principles, the Organisation for Economic Co-operation and Development (OECD) principles for artificial intelligence (AI) and the proposed European Union (EU) Artificial Intelligence Act. The authors argue that AI tools can revolutionize AML/CFT and asset recovery, but there is a need to strike a balance between optimizing AML efficiency and safeguarding fundamental rights.
Design/methodology/approach
This paper draws on reports, legislation, legal scholarships and other open-source data on the digital transformation of AML/CFT, particularly the deployment of AI in this context.
Findings
A new regulatory framework with robust safeguards is necessary to mitigate the risks associated with the use of new technologies in the AML context.
Originality/value
This study is one of the first to examine the use of AI in the AML/CFT context in light of the FATF San Jose principles, the OECD AI principles and the proposed EU AI Act.
Subject
Law,General Economics, Econometrics and Finance,Public Administration
Reference52 articles.
1. Big data surveillance across fields: algorithmic governance for policing and regulation;Big Data and Society,2022
2. The identity challenge in finance: from analogue identity to digitized identification to digital KYC utilities;European Business Organization Law Review,2019
3. Do AI-based anti-money laundering (AML) systems violate European fundamental rights?;International Data Privacy Law,2021
4. Brynjolfsson, E. and McAfee, A. (2017), “What’s driving the machine learning explosion”, Harvard Business Review, available at: https://hbr.org/2017/07/whats-driving-the-machine-learning-explosion (accessed 1 March 2023).
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献