We introduce a simple numerical algorithm to study banking systems subject to credit risk. The algorithm is based on a model that is completely defined by only two parameters. We start with a description of banks’ balance sheets and incorporate two features to account for connectivity effects: (i) interbank loans and (ii) correlated exposures to a common universe of loans. The driving force behind the model is the progressive deterioration of the loan portfolios to which the banks are exposed. This effect is modeled using a Gaussian copula-based approach. This method is useful to identify the weaknesses of a given banking network and to assess the merits of different potential regulatory interventions by the central bank. In addition, we present several metrics that provide important information regarding the resilience of the network. In short, this algorithm is a valuable diagnostic tool that can help regulators before and during a financial crisis. An example based on the banking system of a Latin American country demonstrates the usefulness of this approach.