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

Lucas Potin (), Rosa Figueiredo (), Vincent Labatut () and Christine Largeron ()
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Lucas Potin: LIA - Laboratoire Informatique d'Avignon - AU - Avignon Université - Centre d'Enseignement et de Recherche en Informatique - CERI
Rosa Figueiredo: LIA - Laboratoire Informatique d'Avignon - AU - Avignon Université - Centre d'Enseignement et de Recherche en Informatique - CERI
Vincent Labatut: LIA - Laboratoire Informatique d'Avignon - AU - Avignon Université - Centre d'Enseignement et de Recherche en Informatique - CERI
Christine Largeron: LabHC - Laboratoire Hubert Curien - IOGS - Institut d'Optique Graduate School - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique

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Abstract: 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.

Keywords: Pattern Mining; Graph Classification; Public Procurement; Fraud Detection (search for similar items in EconPapers)
Date: 2023-09-18
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mac
Note: View the original document on HAL open archive server: https://hal.science/hal-04131485
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Published in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2023 (ECML PKDD), Sep 2023, Torino, Italy. ⟨10.1007/978-3-031-43427-3_5⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04131485

DOI: 10.1007/978-3-031-43427-3_5

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