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...

Payments and market infrastructures are the backbone of modern financial systems and play a key role in the economy. One of their main goals is to manage systemic risk, especially in the case of systemically important payment systems (SIPS) serving interbank funds transfers. We develop an...

We develop an unsupervised methodology to group payments and identify possible anomalies. With our methodology, we identify clusters based on a set of network features, using transactional (unlabeled) information from a systemically important payment system of El Salvador. We first preprocess...