SAFE AI models to measure the impact of ESG factors on credit ratings

Artificial Intelligence methods, based on machine learning from data, are rapidly changing financial services, and credit lending in particular, complementing bank lending with platform lending.While financial technologies improve user experience, and possibly lower costs, they may increase risks and, in particular, the model risks that derive from inaccurate credit rating assessments.In this paper we will show how to reduce model risks, using a S.A.F.E. statistical learning model, that is: taking environmental, social and governance factors into account, to improve Sustainability, building a model for which ESG factors do predict credit ratings, to improve their Accuracy, dealing with data inconsistencies, to improve Fairness; merging ESG scores into one mixture, to improve Explainability.