This Occasional paper from RUSI on 16th November reviews the efforts of national authorities to harness the opportunities of data analytics for anti-money-laundering purposes. It sets out potential methods and their benefits, as well as other factors that should be considered to ensure the proportionality and sustainability of innovative approaches. The cases reviewed as part of this research illustrate the value of data analytics for AML supervision and enforcement. For the purposes of this research, the term ‘data analytics’ refers to methods allowing users to turn data into knowledge that would not be revealed through a human review of the data in question. This includes traditional statistical methods and more recent developments relating to ‘big data’ or machine learning. Its conclusions include that supervisors, FIU and law enforcement agencies should adopt a strategy to identify and harness opportunities for the use of data analytics in an AML context; and that bodies such as FATF and the IMF should more systematically review to what extent member countries have considered and harnessed the potential of data analytics for AML intelligence, supervision and enforcement purposes, which will allow for peer learning.