On October 19, the Institute of International Finance (“IIF“) published a report on machine learning in anti-money laundering (“AML“). The report, which isn’t publicly available, sets out a summary of the IIF’s key findings from a survey of 59 institutions (54 banks and 5 insurers).

The IIF explains that as the level of undetected illicit funds in the financial services sector remains too high, firms are increasingly turning to new technologies, including machine learning, to address the issue. The IIF found that 69% of firms surveyed already use or experiment with machine learning techniques. Another 29% indicated that they are planning to apply new analytical techniques in the foreseeable future however, none of the firms surveyed were pursuing machine learning as a means to reduce staff.

The most prominent benefit was increased speed or automation of analysis which allows the AML process to respond to the latest developments in money laundering methods. Challenges were also identified, including uncertainty about regulators’ support for it as part of a firms’ adequate risk-mitigation framework. Overall, the IIF found that the application of machine learning techniques in AML is spreading quickly across the industry, and expects this trend to continue.