An Artificial Intelligence approach to Shadow Rating
This work addresses credit rating prediction for financial analysts, but it appears incremental as it applies existing deep learning methods to a known domain.
The study tackled the problem of predicting credit ratings for global corporate entities using deep learning, finding that artificial neural networks with categorical embeddings achieved adequate accuracy across different rating classes.
We analyse the effectiveness of modern deep learning techniques in predicting credit ratings over a universe of thousands of global corporate entities obligations when compared to most popular, traditional machine-learning approaches such as linear models and tree-based classifiers. Our results show a adequate accuracy over different rating classes when applying categorical embeddings to artificial neural networks (ANN) architectures.