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.


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

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 or view our subscription options here:

You are currently unable to copy this content. Please contact to find out more.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Waterstechnology? View our subscription options

Most read articles loading...

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