Last week, the US Department of Treasury published its 2020 Strategy document that "employs a whole-of-government approach to guide the public and private sectors in addressing 21st century illicit finance challenges." The report highlights the risk-based approach as central to the 2020 Strategy.
Prior to 2018, regulators resisted recommending the use of Machine Learning (ML) based Artificial Intelligence (AI) for AML compliance. There was a mindset shift in mid 2018 indicating that proceeding with caution in implementing AI approaches for AML is appropriate. Regulators realize the adoption of recent innovation, such as the use of AI-ML and robotic process automation (RPA) techniques, enables AML compliance improvements not otherwise attainable. A risk-based approach to compliance, underpinned by AI/Machine Learning, creates opportunities for governance and process refinement as well as identifying potential untapped revenues. Reliance on box-ticking approaches familiar to users of legacy rules-based compliance systems is no longer sufficient.
This is a vexing question and it is at the heart of myriad problems with the current approach to AML/BSA processing. Last year, in conversation with a banking regulator, he observed, that "when a bank buys AML software they also buy an AML business process".