Banks face explainability challenges for AML models

Gaps in data and potential biases need to be accounted for in smart approaches to tackling money laundering

Two years after the US Federal Reserve gave its blessing to banks to pursue artificial intelligence-led approaches to combat financial crime, lenders fear the pendulum might be about to swing back the other way.

Banks have found great early success in piloting machine learning techniques to spot suspicious transactions and identify weaknesses in existing controls, with their power to divine patterns in disparate datasets making them more effective than legacy rules-based systems that find too