Financial crimes pose significant risks to financial institutions, national economies, and society as a whole. The major threats in this domain are addressed through specific regulatory frameworks, such as Anti Money Laundering (AML), Combating the Financing of Terrorism (CFT) and opposing the proliferation of weapons of mass destruction. To mitigate these risks, supervisory authorities impose substantial penalties on institutions that do not adopt effective measures of prevention. Consequently, banks are required to comply with strict regulations designed to detect and prevent such crimes. Although these tasks have traditionally been handled by rule-based Transaction Monitoring (TxM) systems, recent advances in Machine Learning have introduced new paradigms to address this complex challenge.