October 27, 2021

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Natural language processing and transformer models for credit risk – Risk.net

CLICK HERE TO DOWNLOAD THE PDF Emanuel Eckrich, Phil Escott, Rainer Glaser and Christoph Zeiner int.......

CLICK HERE TO DOWNLOAD THE PDF

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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Source: https://www.risk.net/cutting-edge/banking/7868076/natural-language-processing-and-transformer-models-for-credit-risk