Machine Learning in AML Compliance: Italy's Regulatory Framework and the Digital Evolution

Abstract

This thesis provides an analysis of anti-money laundering (AML) regulations and the evolution of compliance, with a focus on Italy's regulatory framework and the role of machine learning. The first chapter provides an overview of the history and evolution of AML regulations, including international and EU legislation. The second chapter examines the concept of compliance and its importance, as well as the supervisory authorities and the supervisory role of the Bank of Italy. The third chapter explores customer due diligence (CDD), including KYC principles and procedures, CDD practices and procedures, and an approach based on risk. The fourth chapter focuses on the digital evolution of compliance and the benefits and challenges of incorporating machine learning into AML measures. The final chapter provides a practical case study of Alter Domus, a multinational financial services provider, and how they have implemented machine learning in their AML compliance program. This thesis concludes that machine learning has the potential to significantly enhance AML compliance and presents a future perspective for the evolution of compliance in the digital era.