After the financial crisis of 2008, anticipating firms' default has garnered increased attention from the scientific community. In this talk, we focus on the interdependence of firms to study the propagation of default among firms belonging to the same supply chain. The propagation may be seen as a contagion among nodes of a complex network or a disease diffusion among adjacent regions on a map. Thus, we explore several techniques belonging to a wide range of research fields: machine learning, complex network analysis used in information theory and Bayesian spatial and spatio-temporal models used in epidemiology and medicine (e.g. fMRI). We apply these approaches on a proprietary dataset of an Italian commercial Bank that records historical defaults, trend features and commercial relationships among firms. We present findings both in terms of knowledge of the process and in terms of model performance improvement with respect to available benchmarks.