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.
Here are four key regulatory areas set for disruption by AI and machine learning:
Prevention of financial fraud
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.
Anti-Money Laundering (AML) increase in Combating the Financing of Terrorism (CFT) detection
Machine learning is able to investigate and examine granular data from structured to unstructured data, allowing it to uncover incongruent relationships, and subsequently detect any strange, complicated patterns of money laundering and the financing of terrorism.
In order to fight against criminal activity, Bdl Capital Management collects detailed data on bank transfers and correlates such information against newspaper articles to identify suspicious patterns.
Improved techniques for risk assessment and prevention
Risk assessment tools, used by financial regulators, are beginning to move into the sphere of Al and machine learning. For example, the US Securities and Exchange Commission (SEC) uses such machines to monitor patterns within their data.
The patterns can then be compared with previous outcomes and determine potential risks in investment manager filings, detect possible fraud and misconduct.
Financial regulator’s use of machine learning would also mean the facilitation of the management of regulatory reporting. In our interview with Nick Cook, he reveals that “we’ve seen better real-time monitoring systems for call centres with automated voice and behavioural analytics”.