Deploying artificial intelligence for anti-money laundering and asset recovery: the dawn of a new era

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.

A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration

“…Support Vector Machine, are used to improve the accuracy of fraud detection. All these algorithms combined creates a robust framework for attacking money laundering (Pavlidis, 2023;Pocher et al, 2022). Ruchay et al (2023) confirmed the identification of suspicious transactions using the random forest algorithm and demonstrated its effectiveness through an accuracy rate of 99%.…”

“…AI has the capacity to analyze data on a large scale and recognize patterns and suspicious activities. This significantly enhances the detection capabilities in AML systems (Pavlidis, 2023). As a result, the use of appropriate algorithms will boost the accuracy of the model up to 0.9999 (Ruchay et al, 2023).…”

“…Figure 2 Artificial intelligence possesses the capability to transform the operational framework within AML systems, enhancing their capacity to efficiently and precisely detect and prevent financial crimes. Through AI-based tools, financial institutions and regulated entities can enhance compliance levels with AML system standards, with improved capabilities in customer identification and transaction monitoring (Pavlidis, 2023). The unique characteristics of each organization require adapting the artificial intelligence tool used to align with its individual algorithms.…”

Editorial: Do androids dream of laundering money?

“…Compliant enterprises operating advanced generative AI systems need to consider accountability and safety (foreseeable risks, adverse impacts and safety concerns), fairness and equity (bias in data pools), transparency and human oversight (AI-generated content with a watermark to identify source and a reporting system for abuses) and finally validity and robustness (testing and cyber-risks including data poisoning) [2]. In the anti–money laundering space, standards from the Financial Action Task Force (San Jose principles) and the Organisation for Economic Co-operation and Development are evolving (Pavlidis, 2024, p. 159).…”