Credit rating is one of the core tools for risk management within financial firms. Ratings are usually provided by specialized agencies which perform an overall study and diagnosis on a given firm’s financial health. Dealing with unrated entities is a common problem, as several risk models rely on the ratings’ completeness, and agencies can not realistically rate every existing company. To solve this, credit rating prediction has been widely studied in academia. However, research in this topic tends to separate models amongst the different rating agencies due to the difference in both rating scales and composition. This work uses transfer learning, via label adaptation, to increase the number of samples for feature selection, and appends these adapted labels as an additional feature to improve the predictive power and stability of previously proposed methods. Accuracy on exact label prediction was improved from 0.30, in traditional models, up to 0.33 in the transfer learning setting. Furthermore, when measuring accuracy with a tolerance of 3 grade notches, accuracy increased almost 0.10, from 0.87 to 0.96. Overall, transfer learning displayed better out-of-sample generalization
