  Money flow models are essential tools to understand different economical
phenomena, like saving propensities and wealth distributions. In spite of their
importance, most of them are based on synthetic transaction networks with
simple topologies, e.g. random or scale-free ones, as the characterisation of
real networks is made difficult by the confidentiality and sensitivity of money
transaction data. Here we present an analysis of the topology created by real
credit card transactions from one of the biggest world banks, and show how
different distributions, e.g. number of transactions per card or amount, have
nontrivial characteristics. We further describe a stochastic model to create
transactions data sets, feeding from the obtained distributions, which will
allow researchers to create more realistic money flow models.
