Suptech, or the use by financial authorities of data collection or advanced data analytics tools enabled by innovative technologies, seems more advanced in the field of anti-money laundering (AML) and combating the financing of terrorism (CFT). In particular, AML/CFT authorities need suptech-enabled advanced data analytics tools to analyse large volumes of information at their disposal. AML/CFT authorities are in general pursuing similar advanced data analytics tools, such as network analysis, natural language processing, text mining and machine learning. These tools increase their ability to detect networks of related transactions, to identify unusual behaviours and, in general, to transform significant amounts of structured and unstructured data into useful information that contributes to their respective processes. Efficiency gains seem to be the number one benefit of advanced data analytics tools, which could help capacity-constrained AML/CFT authorities. However, the use of these innovative technologies gives rise to a number of challenges, including computational capacity constraints and data privacy and confidentiality issues.
This paper aims to explore the various data analytics tools used by authorities tasked with AML/CFT responsibilities, as well as their practical experiences in using such tools.