  Banking fraud causes billion-dollar losses for banks worldwide. In fraud
detection, graphs help understand complex transaction patterns and discovering
new fraud schemes. This work explores graph patterns in a real-world
transaction dataset by extracting and analyzing its network motifs. Since
banking graphs are heterogeneous, we focus on heterogeneous network motifs.
Additionally, we propose a novel network randomization process that generates
valid banking graphs. From our exploratory analysis, we conclude that network
motifs extract insightful and interpretable patterns.
