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

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Systematic tools gain favor in fixed income

Automation is enabling systematic strategies in fixed income that were previously reserved for equities trading. The tech gap between the two may be closing, but differences remain.

Why recent failures are a catalyst for DLT’s success

Deutsche Bank’s Mathew Kathayanat and Jie Yi Lee argue that DLT's high-profile failures don't mean the technology is dead. Now that the hype has died down, the path is cleared for more measured decisions about DLT’s applications.

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