How to Use Artificial Intelligence for the Prevention of Money Laundering and Terrorist Financing

This article, written by Claudia Zambrano, Senior Associate at our partners in Peru, Damma Legal Advisors, analyzes how Artificial Intelligence (AI) is revolutionizing the prevention of money laundering and terrorist financing (ML/TF) in the financial sector. While focused on Peruvian law, the content has global relevance, highlighting new opportunities and challenges for institutions and regulators.

How to Use Artificial Intelligence for the Prevention of Money Laundering and Terrorist Financing

Currently, Artificial Intelligence (AI) is one of the emerging new technologies that has transitioned from a phase of discovery about its functionalities and uses to a phase of application in real-life cases. Day by day, new ways of using this tool in professional life are being identified, aiming to make our tasks more efficient.

One of the recently identified areas of AI application is the prevention of money laundering and terrorist financing (ML/TF) crimes in the financial banking sector. This sector, due to the inherent risk of processing a high volume of transactional operations, has been involved in controversies in recent years related to the failure to detect criminal activities. To implement better systems for the detection and prevention of ML/TF crimes, financial entities are launching projects aimed at training their staff in the use of AI with the following goals: (i) training AI to identify transactions with characteristics related to ML/TF; (ii) identifying warning signs and filtering out false positives; (iii) efficiently analyzing daily transaction data, among others.

Some countries are incentivizing ML/TF prevention through the application of AI, even though it is not explicitly mentioned. This is exemplified by the Anti-Money Laundering Act (AML Act) passed by the U.S. Congress in 2021, which delegates to the Treasury Department the preparation and execution of conferences to discuss the application of new technologies (such as AI) in the prevention and detection of financial crimes and other illicit activities. From the perspective of the private sector, following a controversy in 2017 when it was involved in a money laundering scheme, HSBC, a globally present bank, has been progressively implementing ML/TF controls supported by AI.

In Peru, the Superintendencia de Banca y Seguros (SBS - Superintendent of Banks and Insurance Companies) requires supervised entities to implement detection and prevention systems, especially when certain commercial sectors are susceptible to providing services that facilitate ML/TF. Resolution SBS No. 789-2018, which approves the regulatory framework for ML/TF prevention (the Regulation), under the supervision of UIF-Perú (Financial Intelligence Unit - UIF), is mandatory for banks, credit companies, municipal savings banks, loan companies, currency exchange houses, and others (the Obligated Subjects).

The Obligated Subjects must implement an anti-money laundering and terrorist financing prevention system (SPLAFT) that must, at a minimum, include the approval of policies and procedures for managing ML/TF risks, as well as the approval of procedures to prevent and detect unusual transactions and to prevent, detect, and report to UIF-Perú any suspicious transactions potentially linked to ML/TF. The Regulation defines suspicious transactions as those transactions carried out or attempted that, due to their amount or characteristics, do not align with the economic activity of the client of the Obligated Subjects, or that lack economic foundation; or that, due to their characteristics, reasonably suggest that the Obligated Subjects are being used to transfer, manage, exploit, or invest resources derived from criminal activities or intended to finance them.

Considering the Peruvian ML/TF prevention regulation, AI could be the solution for Obligated Subjects to efficiently comply with their regulatory obligations. AI can be trained to accurately and in real-time identify warning signs (by analyzing the objective characteristics of a transaction) in financial transactions that may be considered suspicious. This task is especially relevant given that Obligated Subjects may handle thousands of transactions daily. Additionally, the appointment of a Compliance Officer responsible for overseeing the proper implementation and functioning of SPLAFT is mandatory. This individual could be responsible for implementing AI in the SPLAFT, leveraging prior experience in supervising SPLAFT to train the AI and lead its implementation within their organization.

It is also worth mentioning that the application of AI in ML/TF prevention aligns with the objectives of Law No. 31814, a law promoting the use of AI for economic and social development in Peru (the AI Law) — one of the first AI regulatory frameworks approved in Latin America. This law encourages the use of AI to improve Peru’s economy and other national economic and social activities. It raises the question of whether specific legislation promoting AI use in the Peruvian financial system is necessary. Nevertheless, current AI regulations already encourage the private sector to seek efficiency in their operations with AI’s support.