The challenge of integrating Artificial Intelligence in Anti-Money Laundering

Abstract

Digital transformation is radically changing the behavior of individuals, businesses and organizations in the financial field. The need to better understand customer needs, offer targeted products and services and manage risk more effectively pushes all operators in the sector to adopt a so-called data-driven model.

A data-driven approach means that business decisions, from marketing strategies to risk management, are based on data analysis and interpretation, rather than intuition or past experience. This involves the ability to collect, process, and use large amounts of data to gain actionable insights and make more informed and accurate decisions.

The data-driven approach is also revolutionizing the anti-money laundering (AML) sector, offering more effective tools to counter money laundering and terrorist financing.

In a scenario where traditional AML methods often rely on predefined rules and manual checks, which can be slow, expensive and ineffective in detecting suspicious activity, generating a high number of false positives and requiring further analysis by experts and increasing operational costs, AI becomes a powerful tool to interpret, learn and act on the basis of the information available. AI, in fact, feeds on data to function: the more data it has available, the more it is able to learn and improve its performance.

This research therefore aims to explore how the implementation of artificial intelligence tools in the field of anti-money laundering and countering the financing of terrorism is the most effective way to improve the performance of a system that, unfortunately, still struggles to identify and isolate resources and capital of criminal origin.

Through an in-depth analysis of current and future application of AI, the benefits in terms of transaction monitoring, money laundering risk assessment, identification of criminal networks and complex money laundering schemes will be assessed.

In addition, challenges related to AI implementation, such as data quality and management, privacy and cybersecurity, and the impact on employment, will be discussed.