New York Fed Eyes Machine Learning to Predict Misreporting

The regulator's goal is to decrease the back and forth during reporting and predict misreporting.

federal-reserve-bank-new-york

The Federal Reserve Bank of New York is turning to machine learning to cut down on the back and forth between the regulator and banks as it looks to predict potential misreporting errors.

During the Waters USA conference in Manhattan, which was held on Dec. 3, Sri Malladi, senior director at the New York Fed’s data and statistics group, said that the regulator’s long-term goal with machine learning is to be able to predict potential issues with banks’ reporting.

“We want to be at the point

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