  In the context of public procurement, several indicators called red flags are
used to estimate fraud risk. They are computed according to certain contract
attributes and are therefore dependent on the proper filling of the contract
and award notices. However, these attributes are very often missing in
practice, which prohibits red flags computation. Traditional fraud detection
approaches focus on tabular data only, considering each contract separately,
and are therefore very sensitive to this issue. In this work, we adopt a
graph-based method allowing leveraging relations between contracts, to
compensate for the missing attributes. We propose PANG (Pattern-Based Anomaly
Detection in Graphs), a general supervised framework relying on pattern
extraction to detect anomalous graphs in a collection of attributed graphs.
Notably, it is able to identify induced subgraphs, a type of pattern widely
overlooked in the literature. When benchmarked on standard datasets, its
predictive performance is on par with state-of-the-art methods, with the
additional advantage of being explainable. These experiments also reveal that
induced patterns are more discriminative on certain datasets. When applying
PANG to public procurement data, the prediction is superior to other methods,
and it identifies subgraph patterns that are characteristic of fraud-prone
situations, thereby making it possible to better understand fraudulent
behavior.
