Natural language processing and transformer models for credit risk

News feeds are factored into models to predict credit events

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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

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