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18 septembre 2023
- handle: 10670/1.g4vl7a
- hal: hal-04131485
- ARXIV: 2306.10857
- doi: 10.1007/978-3-031-43427-3_5
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info:eu-repo/semantics/altIdentifier/arxiv/2306.10857
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-43427-3_5
info:eu-repo/semantics/OpenAccess
Sujets proches
Plots (Diagrams) Graphs Diagrams, charts, etc Graphics Graphs Public procurement Government procurement Procurement, Government Public purchasing Deceit Commercial fraud Misrepresentation (Crime) Pattern ModelCiter ce document
Lucas Potin et al., « Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement », HAL-SHS : économie et finance, ID : 10.1007/978-3-031-43427-3_5
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Résumé
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.