Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams. (arXiv:2304.10740v1 [q-fin.GN])

Knowing which factors are significant in credit rating assignment leads to
better decision-making. However, the focus of the literature thus far has been
mostly on structured data, and fewer studies have addressed unstructured or
multi-modal datasets. In this paper, we present an analysis of the most
effective architectures for the fusion of deep learning models for the
prediction of company credit rating classes, by using structured and
unstructured datasets of different types. In these models, we tested different
combinations of …

analysis architectures arxiv credit data datasets data streams decision deep learning focus fusion leads literature making numerical paper prediction strategies structured data studies text types unstructured