We present a general supervised machine-learning methodology to represent the payment behavior of financial institutions starting from a database of transactions in the Colombian large-value payment system. The methodology learns a feedforward artificial neural network parameterization to represent the payment patterns through 113 features corresponding to financial institutions’ contribution to payments, funding habits, payment timing, payment concentration, centrality in the payment network, and systemic effects due to failure to pay. We then use the representation to compare the coherence of out-of-sample payment patterns of the same institution to its characteristic patterns. The performance is remarkable, with an out-of-sample classification error around three percent. The performance is robust to reductions in the number of features by unsupervised feature selection. In addition, we confirm that network centrality and systemic effect features definitively contribute to enhancing the performance of the methodology. For financial authorities, this is a major step towards the automated detection of individual financial institutions’ anomalous behaviors in payment systems.