The credit risk of large financial institutions is highly interdependent as a result of a number of linkages between financial entities such as exposure to common asset classes and counterparty risk. In this work we propose a novel methodology to study credit risk interconnectedness in large panels of financial institutions. Building upon the standard reduced form framework for credit risk, we introduce a model in which bank defaults can be triggered both by systematic economy wide and idiosyncratic bank specific shocks. The idiosyncratic shocks are assumed to have a sparse conditional dependence structure that we call the bank credit risk network. We then develop an estimation strategy based on Lasso regression that allows to detect and estimate network linkages from CDS data. We apply this technique to analyse the interdependence of large European financial institutions between 2006 and 2013. Results show that the credit risk network captures a substantial amount of dependence in addition to what is explained by systematic factors. Joint work with Christina Hans and Eulalia Nualart (Universitat Pompeu Fabra and Barcelona GSE). Keywords: Credit Risk, European Financial Crisis, Networks, CDS, Lasso.