AML models face explainability challenges

Data gaps and potential biases must be accounted for in approaches to tackling money laundering.

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

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact [email protected] or view our subscription options here:

You are currently unable to copy this content. Please contact [email protected] to find out more.

To continue reading...

You need to sign in to use this feature. If you don’t have a WatersTechnology account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: