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AML/CFT supervision and financial intelligence functions have different mandates. Authorities with AML supervision functions are expected to ensure compliance by financial institutions with requirements to combat money laundering (ML) and terrorist financing (TF). Authorities with financial intelligence functions, ie financial intelligence units (FIUs), meanwhile, are expected to serve as national centres for the receipt and analysis of suspicious transaction reports and other information relevant to money laundering, and to disseminate the results of that analysis. FIUs sometimes also have AML supervision functions.
Both AML/CFT supervisors and FIUs need advanced data analytics tools to analyse the large volumes of information at their disposal.
The difference in mandates does not seem to affect the types of advanced data analytics tools the AML/CFT authorities are pursuing. AML/CFT authorities covered in the paper are in general pursuing similar advanced data analytics tools, such as network analysis, natural language processing, text mining and machine learning.
Authorities have used different strategies to develop these tools.
Efficiency gains seem to be the number one benefit of advanced data analytics tools, which could help capacity-constrained AML/CFT authorities.
The benefits that these tools bring are particularly important for jurisdictions that have been heavily impacted by the unintended consequences of AML/CFT international standards, particularly de-risking.
However, the use of these innovative technologies gives rise to a number of challenges. First, computational capacity may be an issue, since these tools deal with large volumes of data. Second, data privacy and confidentiality requirements provide safeguards that AML/CFT authorities must consider in using certain data and external resources in developing data analytics tools. Third, assessing the effectiveness of these tools might be challenging, in particular for FIUs given the necessary time to prove the occurrence of a money laundering activity. Finally, tools based on supervised machine learning could lose their effectiveness over time, especially if not regularly updated with new training data, given the capacity of criminal organisations to change their behaviour in order to avoid detection.
There is scope for information-sharing among AML/CFT authorities on the data analytics tools they are developing or using in order to promote peer learning. Although the data analytics tools used by AML/CFT authorities are tweaked to reflect their mandates, the underlying methodologies of these tools are quite similar. There are therefore opportunities for peer learning through regular exchange of information and sharing of experiences on the development and use of these tools.
AML/CFT authorities that are just starting to develop their data infrastructure have a “late mover” advantage and may find it easier to integrate advanced data analytics tools.
ML/TF risks have international reach, so development of data analytics tools that are international in scope should be considered. The tools discussed in this paper are all national in scope. Money laundering, however, is an international issue, and criminal organisations tend to exploit loopholes anywhere in the world. Therefore, a strong argument could be made for international cooperation and collaboration in terms of developing data analytics tools with an international coverage.