  There is an abundance of temporal and non-temporal data in banking (and other
industries), but such temporal activity data can not be used directly with
classical machine learning models. In this work, we perform extensive feature
extraction from the temporal user activity data in an attempt to predict user
visits to different branches and credit card up-selling utilizing user
information and the corresponding activity data, as part of \emph{ECML/PKDD
Discovery Challenge 2016 on Bank Card Usage Analysis}. Our solution ranked
\nth{4} for \emph{Task 1} and achieved an AUC of \textbf{$0.7056$} for
\emph{Task 2} on public leaderboard.
