In this paper the authors focus on credit connections as a potential source of systemic risk. In particular, they seek to answer the following question: how do we find densely connected subsets of nodes within a credit network? The question is relevant for policy, since these subsets are likely to channel any shock affecting the network. As it turns out, a reliable answer can be obtained with the aid of complex network theory. In particular, the authors show how it is possible to take advantage of the ''community detection'' network literature. The proposed answer entails two subsequent steps. Firstly, the authors verify the hypothesis that the network under study truly has communities. Secondly, they devise a reliable algorithm to find those communities. In order to be sure that a given algorithm works, they test it over a sample of random benchmark networks with known communities. To overcome the limitation of existing benchmarks, the authors introduce a new model and test alternative algorithms, obtaining very good results with an adapted spectral decomposition method. To illustrate this method they provide a community description of the Japanese bank-firm credit network, getting evidence of a strengthening of communities over time and finding support for the well-known Japanese main ''bank'' system. Thus, the authors find comfort both from simulations and from real data on the possibility to apply community detection methods to credit markets. They believe that this method can fruitfully complement the study of contagious defaults. Since network risk depends crucially on community structure, their results suggest that policy maker should identify systemically important communities, i.e. those able extend the initial shock to the entire system.