  The detection of frauds in credit card transactions is a major topic in
financial research, of profound economic implications. While this has hitherto
been tackled through data analysis techniques, the resemblances between this
and other problems, like the design of recommendation systems and of diagnostic
/ prognostic medical tools, suggest that a complex network approach may yield
important benefits. In this contribution we present a first hybrid data mining
/ complex network classification algorithm, able to detect illegal instances in
a real card transaction data set. It is based on a recently proposed network
reconstruction algorithm that allows creating representations of the deviation
of one instance from a reference group. We show how the inclusion of features
extracted from the network data representation improves the score obtained by a
standard, neural network-based classification algorithm; and additionally how
this combined approach can outperform a commercial fraud detection system in
specific operation niches. Beyond these specific results, this contribution
represents a new example on how complex networks and data mining can be
integrated as complementary tools, with the former providing a view to data
beyond the capabilities of the latter.
