  Graph-based Neural Networks (GNNs) are recent models created for learning
representations of nodes (and graphs), which have achieved promising results
when detecting patterns that occur in large-scale data relating different
entities. Among these patterns, financial fraud stands out for its
socioeconomic relevance and for presenting particular challenges, such as the
extreme imbalance between the positive (fraud) and negative (legitimate
transactions) classes, and the concept drift (i.e., statistical properties of
the data change over time). Since GNNs are based on message propagation, the
representation of a node is strongly impacted by its neighbors and by the
network's hubs, amplifying the imbalance effects. Recent works attempt to adapt
undersampling and oversampling strategies for GNNs in order to mitigate this
effect without, however, accounting for concept drift. In this work, we conduct
experiments to evaluate existing techniques for detecting network fraud,
considering the two previous challenges. For this, we use real data sets,
complemented by synthetic data created from a new methodology introduced here.
Based on this analysis, we propose a series of improvement points that should
be investigated in future research.
