With increasing institutional use of Al and machine learning methods, more and more industry regulators are making use of such software to provide financial stability through services and systemic risk surveillance. Central to the UK Financial Conduct Authority's (FCA) agenda is their intent to develop "both their technology side – cloud analytics – as well as building out our human side – our data science capability" to allow an "osmotic" expansion across the wider organisation, "effectively enabling us to leverage machine learning," says head of regtech and advanced analytics at FCA, Nick Cook. Investigations of suspicious transactions are highly time-consuming, and often, due to overly-defensive mechanisms, fall victim of unsuccessful outcomes. Through the identification of intricate patterns, machine learning would flag up serious, rather than harmless transactions, and, consequently require the attention of supervisors. For instance, with the aid of Al and machine learning, the Monetary Authority of Singapore (MAS) examines suspicious transactions that require further analysis and attention, which, in turn, enables supervisors to spend their time and resources on higher risk transactions.