Natural language processing and transformer models for credit risk

News feeds are factored into models to predict credit events


Emanuel Eckrich, Phil Escott, Rainer Glaser and Christoph Zeiner introduce an innovative approach to using deep learning, natural language processing and transformer models for the generation of highly predictive credit risk models. The underlying capability to efficiently build models that distill a predictive signal from unfiltered and unstructured data sets is universal, but its efficiency is illustrated in the context of using news feeds to predict credit risk

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 View our subscription options

You need to sign in to use this feature. If you don’t have a 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