Credit risk models for small and medium businesses (SMEs) are cumbersome for lenders, inefficient and bias prone. Dr. Cristián Bravo’s research focuses on developing and applying data science methodologies to improve credit risk analytics. It also aims to design fair and transparent models to determine the probability of default by SMEs based on multimodal data (images, text, and traditional structured data). These models will satisfy legal requirements regarding unfair discrimination regulations and model explainability. It will do so by exploring counterfactual fairness, i.e. fair models with a known unfair impact variable, and by creating propagation methods that focus on studying how the models learn what they learn and visualizing these effects.