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In the context of public procurement, several indicators called red flags areused to estimate fraud risk. They are computed according to certain contractattributes and are therefore dependent on the proper filling of the contractand award notices. However, these attributes are very often missing inpractice, which prohibits red flags computation. Traditional fraud detectionapproaches focus on tabular data only, considering each contract separately,and are therefore very sensitive to this issue. In this work, we adopt agraph-based method allowing leveraging relations between contracts, tocompensate for the missing attributes. We propose PANG (Pattern-Based AnomalyDetection in Graphs), a general supervised framework relying on patternextraction to detect anomalous graphs in a collection of attributed graphs.Notably, it is able to identify induced subgraphs, a type of pattern widelyoverlooked in the literature. When benchmarked on standard datasets, itspredictive performance is on par with state-of-the-art methods, with theadditional advantage of being explainable. These experiments also reveal thatinduced patterns are more discriminative on certain datasets. When applyingPANG to public procurement data, the prediction is superior to other methods,and it identifies subgraph patterns that are characteristic of fraud-pronesituations, thereby making it possible to better understand fraudulentbehavior.

Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement