  Financial institutions use clients' payment transactions in numerous banking
applications. Transactions are very personal and rich in behavioural patterns,
often unique to individuals, which make them equivalent to personally
identifiable information in some cases. In this paper, we generate synthetic
transactional profiles using machine learning techniques with the goal to
preserve both data utility and privacy. A challenge we faced was to deal with
sparse vectors due to the few spending categories a client uses compared to all
the ones available. We measured data utility by calculating common insights
used by the banking industry on both the original and the synthetic data-set.
Our approach shows that neural network models can generate valuable synthetic
data in such context. Finally, we tried privacy-preserving techniques and
observed its effect on models' performances.
